{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "strange-cartoon",
   "metadata": {},
   "source": [
    "# TimeEval result comaprison\n",
    "\n",
    "Reads the results from two TimeEval runs and compiles a small report. The report compares the results of the default parameter executions to those with optimized parameters. Change the constants and the configuration to compile the report for another TimeEval run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "elegant-fellow",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Automatically reload packages:\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "directed-instruction",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c6f54f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "if default_use_plotly:\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "\n",
    "def plot_bars(df, columns=[], agg=\"mean\", use_plotly=default_use_plotly, title=\"\"):\n",
    "    if len(columns) == 0:\n",
    "        raise ValueError(\"No columns selected!\")\n",
    "    df_tmp = df.pivot_table(index=\"algorithm\", values=columns, aggfunc=agg)\n",
    "    df_tmp = df_tmp.sort_values(by=columns[0], ascending=True, na_position=\"first\")\n",
    "    df_tmp.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    if use_plotly:\n",
    "        return plot_bars_plotly(df_tmp, columns, title)\n",
    "    else:\n",
    "        return plot_bars_plt(df_tmp, columns, title)\n",
    "\n",
    "\n",
    "def plot_bars_plotly(df, columns=[], title=\"\"):\n",
    "    fig = px.bar(df, x=\"algorithm\", y=columns, barmode=\"group\")\n",
    "    fig.update_xaxes(tickangle=45)\n",
    "    fig.update_layout(title={\"text\":title, \"xanchor\": \"center\", \"x\": 0.5}, yaxis_title=\"Metric\", showlegend=True)\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_bars_plt(df, columns=[], title=\"\"):\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    if(len(columns)) > 2:\n",
    "        raise ValueError(\"Cannot plot more than two bar charts!\")\n",
    "    for i, c in enumerate(columns):\n",
    "        ax.bar(df[\"algorithm\"], df[c], align=\"edge\", width=-0.3 if i == 0 else 0.3, label=c)\n",
    "    ax.set_xticklabels(df[\"algorithm\"], rotation=-45, ha=\"left\", rotation_mode=\"anchor\")\n",
    "    ax.set_title(title)\n",
    "    ax.legend()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "synthetic-motivation",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "textile-preview",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('../../results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('../../results/2021-10-18_runtime-gutentag')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"../../data\") / \"test-cases\"\n",
    "result_root_path = Path(\"../../results\")\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "result_paths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surface-tanzania",
   "metadata": {},
   "source": [
    "Select a results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "spiritual-emergency",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading reference results from /home/sebastian/Documents/Projects/akita/results/2021-09-27_default-params-1&2&3&4-merged\n",
      "Reading optimized results from /home/sebastian/Documents/Projects/akita/results/2021-10-18_runtime-gutentag\n"
     ]
    }
   ],
   "source": [
    "reference_result_path = result_root_path / \"2021-09-27_default-params-1&2&3&4-merged\"\n",
    "optim_result_path = result_root_path / \"2021-10-18_runtime-gutentag\"\n",
    "\n",
    "# load reference results\n",
    "print(f\"Reading reference results from {reference_result_path.resolve()}\")\n",
    "df_ref = pd.read_csv(reference_result_path / \"results.csv\")\n",
    "df_ref[\"dataset_name\"] = df_ref[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load optimized algorithms result path\n",
    "print(f\"Reading optimized results from {optim_result_path.resolve()}\")\n",
    "df_optim = pd.read_csv(optim_result_path / \"results.csv\")\n",
    "df_optim[\"dataset_name\"] = df_optim[\"dataset\"].str.split(\".\").str[0]\n",
    "df_optim.drop_duplicates(keep=\"first\", inplace=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-board",
   "metadata": {},
   "source": [
    "Only consider the best run for each `algorithm`-`dataset`-combination (over all `hyper_params` and `repetition`s) for the analysis in this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "single-agent",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_groups(group):\n",
    "    if len(group) > 1:\n",
    "        group = group.sort_values(by=\"ROC_AUC\", ascending=False)\n",
    "    return group[:1]\n",
    "\n",
    "df_grouped = df_ref.groupby(by=[\"algorithm\", \"collection\", \"dataset\"])\n",
    "df_grouped = df_grouped.apply(filter_groups)\n",
    "df_grouped.reset_index(drop=True, inplace=True)\n",
    "df_ref = df_grouped\n",
    "df_ref = df_ref.sort_values(by=[\"algorithm\", \"dataset\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "julian-produce",
   "metadata": {},
   "source": [
    "## Compare TimeEval results on GutenTAG datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "disturbed-impact",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>status</th>\n",
       "      <th>AVERAGE_PRECISION</th>\n",
       "      <th>PR_AUC</th>\n",
       "      <th>RANGE_PR_AUC</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>hyper_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-combined-diff-1</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089392</td>\n",
       "      <td>0.531476</td>\n",
       "      <td>153.115361</td>\n",
       "      <td>{\"distance_metric\": \"euclidean\", \"max_lag\": 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-combined-diff-2</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066603</td>\n",
       "      <td>0.452896</td>\n",
       "      <td>165.418194</td>\n",
       "      <td>{\"distance_metric\": \"euclidean\", \"max_lag\": 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-combined-diff-3</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>0.214069</td>\n",
       "      <td>210.531216</td>\n",
       "      <td>{\"distance_metric\": \"euclidean\", \"max_lag\": 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-diff-count-1</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>0.113232</td>\n",
       "      <td>115.612526</td>\n",
       "      <td>{\"distance_metric\": \"euclidean\", \"max_lag\": 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-diff-count-2</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011949</td>\n",
       "      <td>0.507001</td>\n",
       "      <td>147.730167</td>\n",
       "      <td>{\"distance_metric\": \"euclidean\", \"max_lag\": 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13101</th>\n",
       "      <td>normal</td>\n",
       "      <td>sinus-type-pattern-shift</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13102</th>\n",
       "      <td>normal</td>\n",
       "      <td>sinus-type-pattern</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13103</th>\n",
       "      <td>normal</td>\n",
       "      <td>sinus-type-platform</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13104</th>\n",
       "      <td>normal</td>\n",
       "      <td>sinus-type-trend</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13105</th>\n",
       "      <td>normal</td>\n",
       "      <td>sinus-type-variance</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13106 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      algorithm              dataset_name     status  AVERAGE_PRECISION  \\\n",
       "0         ARIMA       cbf-combined-diff-1  Status.OK                NaN   \n",
       "1         ARIMA       cbf-combined-diff-2  Status.OK                NaN   \n",
       "2         ARIMA       cbf-combined-diff-3  Status.OK                NaN   \n",
       "3         ARIMA          cbf-diff-count-1  Status.OK                NaN   \n",
       "4         ARIMA          cbf-diff-count-2  Status.OK                NaN   \n",
       "...         ...                       ...        ...                ...   \n",
       "13101    normal  sinus-type-pattern-shift  Status.OK               0.01   \n",
       "13102    normal        sinus-type-pattern  Status.OK               0.01   \n",
       "13103    normal       sinus-type-platform  Status.OK               0.01   \n",
       "13104    normal          sinus-type-trend  Status.OK               0.01   \n",
       "13105    normal       sinus-type-variance  Status.OK               0.01   \n",
       "\n",
       "       PR_AUC  RANGE_PR_AUC   ROC_AUC  execute_main_time  \\\n",
       "0         NaN      0.089392  0.531476         153.115361   \n",
       "1         NaN      0.066603  0.452896         165.418194   \n",
       "2         NaN      0.001155  0.214069         210.531216   \n",
       "3         NaN      0.005247  0.113232         115.612526   \n",
       "4         NaN      0.011949  0.507001         147.730167   \n",
       "...       ...           ...       ...                ...   \n",
       "13101   0.505      0.505000  0.500000           0.000031   \n",
       "13102   0.505      0.505000  0.500000           0.000016   \n",
       "13103   0.505      0.505000  0.500000           0.000021   \n",
       "13104   0.505      0.505000  0.500000           0.000016   \n",
       "13105   0.505      0.505000  0.500000           0.000017   \n",
       "\n",
       "                                            hyper_params  \n",
       "0      {\"distance_metric\": \"euclidean\", \"max_lag\": 10...  \n",
       "1      {\"distance_metric\": \"euclidean\", \"max_lag\": 10...  \n",
       "2      {\"distance_metric\": \"euclidean\", \"max_lag\": 10...  \n",
       "3      {\"distance_metric\": \"euclidean\", \"max_lag\": 10...  \n",
       "4      {\"distance_metric\": \"euclidean\", \"max_lag\": 10...  \n",
       "...                                                  ...  \n",
       "13101                                                 {}  \n",
       "13102                                                 {}  \n",
       "13103                                                 {}  \n",
       "13104                                                 {}  \n",
       "13105                                                 {}  \n",
       "\n",
       "[13106 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ref[[\"algorithm\", \"dataset_name\", \"status\", \"AVERAGE_PRECISION\", \"PR_AUC\", \"RANGE_PR_AUC\", \"ROC_AUC\", \"execute_main_time\", \"hyper_params\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7238d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>status</th>\n",
       "      <th>AVERAGE_PRECISION</th>\n",
       "      <th>PR_AUC</th>\n",
       "      <th>RANGE_PR_AUC</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>hyper_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-combined-diff-1</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.454742</td>\n",
       "      <td>0.465248</td>\n",
       "      <td>0.453215</td>\n",
       "      <td>0.815319</td>\n",
       "      <td>78.018341</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-combined-diff-2</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.068871</td>\n",
       "      <td>0.061556</td>\n",
       "      <td>0.077541</td>\n",
       "      <td>0.480169</td>\n",
       "      <td>134.534048</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-combined-diff-3</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.241877</td>\n",
       "      <td>0.127965</td>\n",
       "      <td>0.136431</td>\n",
       "      <td>0.955978</td>\n",
       "      <td>130.194427</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-diff-count-1</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.014368</td>\n",
       "      <td>0.008516</td>\n",
       "      <td>0.016521</td>\n",
       "      <td>0.439091</td>\n",
       "      <td>72.464707</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>cbf-diff-count-2</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.007510</td>\n",
       "      <td>0.012396</td>\n",
       "      <td>0.343218</td>\n",
       "      <td>107.627002</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10750</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>sinus-type-pattern-shift</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.370137</td>\n",
       "      <td>0.362852</td>\n",
       "      <td>0.368447</td>\n",
       "      <td>0.837476</td>\n",
       "      <td>37.700177</td>\n",
       "      <td>{\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10751</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>sinus-type-pattern</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.176112</td>\n",
       "      <td>0.171726</td>\n",
       "      <td>0.174526</td>\n",
       "      <td>0.448554</td>\n",
       "      <td>33.797723</td>\n",
       "      <td>{\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10752</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>sinus-type-platform</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.582908</td>\n",
       "      <td>{\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10753</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>sinus-type-trend</td>\n",
       "      <td>Status.ERROR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>sinus-type-variance</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.999901</td>\n",
       "      <td>0.999900</td>\n",
       "      <td>0.994974</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>37.240455</td>\n",
       "      <td>{\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10755 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      algorithm              dataset_name        status  AVERAGE_PRECISION  \\\n",
       "0         ARIMA       cbf-combined-diff-1     Status.OK           0.454742   \n",
       "1         ARIMA       cbf-combined-diff-2     Status.OK           0.068871   \n",
       "2         ARIMA       cbf-combined-diff-3     Status.OK           0.241877   \n",
       "3         ARIMA          cbf-diff-count-1     Status.OK           0.014368   \n",
       "4         ARIMA          cbf-diff-count-2     Status.OK           0.011311   \n",
       "...         ...                       ...           ...                ...   \n",
       "10750    VALMOD  sinus-type-pattern-shift     Status.OK           0.370137   \n",
       "10751    VALMOD        sinus-type-pattern     Status.OK           0.176112   \n",
       "10752    VALMOD       sinus-type-platform     Status.OK           1.000000   \n",
       "10753    VALMOD          sinus-type-trend  Status.ERROR                NaN   \n",
       "10754    VALMOD       sinus-type-variance     Status.OK           0.999901   \n",
       "\n",
       "         PR_AUC  RANGE_PR_AUC   ROC_AUC  execute_main_time  \\\n",
       "0      0.465248      0.453215  0.815319          78.018341   \n",
       "1      0.061556      0.077541  0.480169         134.534048   \n",
       "2      0.127965      0.136431  0.955978         130.194427   \n",
       "3      0.008516      0.016521  0.439091          72.464707   \n",
       "4      0.007510      0.012396  0.343218         107.627002   \n",
       "...         ...           ...       ...                ...   \n",
       "10750  0.362852      0.368447  0.837476          37.700177   \n",
       "10751  0.171726      0.174526  0.448554          33.797723   \n",
       "10752  1.000000      0.995026  1.000000          33.582908   \n",
       "10753       NaN           NaN       NaN                NaN   \n",
       "10754  0.999900      0.994974  0.999999          37.240455   \n",
       "\n",
       "                                            hyper_params  \n",
       "0      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "1      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "2      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "3      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "4      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "...                                                  ...  \n",
       "10750  {\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...  \n",
       "10751  {\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...  \n",
       "10752  {\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...  \n",
       "10753  {\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...  \n",
       "10754  {\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...  \n",
       "\n",
       "[10755 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_optim[[\"algorithm\", \"dataset_name\", \"status\", \"AVERAGE_PRECISION\", \"PR_AUC\", \"RANGE_PR_AUC\", \"ROC_AUC\", \"execute_main_time\", \"hyper_params\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "casual-rubber",
   "metadata": {},
   "source": [
    "#### Compute the score differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "incident-clarity",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>collection</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>AVERAGE_PRECISION_diff</th>\n",
       "      <th>PR_AUC_diff</th>\n",
       "      <th>RANGE_PR_AUC_diff</th>\n",
       "      <th>ROC_AUC_diff</th>\n",
       "      <th>train_main_time_diff</th>\n",
       "      <th>execute_main_time_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-combined-diff-1</td>\n",
       "      <td>0.454742</td>\n",
       "      <td>0.465248</td>\n",
       "      <td>0.363823</td>\n",
       "      <td>0.283843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-75.097020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-combined-diff-2</td>\n",
       "      <td>0.068871</td>\n",
       "      <td>0.061556</td>\n",
       "      <td>0.010938</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-30.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-combined-diff-3</td>\n",
       "      <td>0.241877</td>\n",
       "      <td>0.127965</td>\n",
       "      <td>0.135276</td>\n",
       "      <td>0.741909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80.336789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-diff-count-1</td>\n",
       "      <td>0.014368</td>\n",
       "      <td>0.008516</td>\n",
       "      <td>0.011273</td>\n",
       "      <td>0.325859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-43.147819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-diff-count-2</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.007510</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>-0.163783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-40.103166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>k-Means</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-pattern-shift</td>\n",
       "      <td>-0.032091</td>\n",
       "      <td>-0.032544</td>\n",
       "      <td>-0.008014</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.251641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10357</th>\n",
       "      <td>k-Means</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-pattern</td>\n",
       "      <td>0.999901</td>\n",
       "      <td>0.999900</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.553484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10358</th>\n",
       "      <td>k-Means</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-platform</td>\n",
       "      <td>-0.027792</td>\n",
       "      <td>-0.024494</td>\n",
       "      <td>0.086535</td>\n",
       "      <td>-0.000753</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.716808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10359</th>\n",
       "      <td>k-Means</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-trend</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.612107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10360</th>\n",
       "      <td>k-Means</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-variance</td>\n",
       "      <td>0.999019</td>\n",
       "      <td>0.999014</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.290748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10361 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      algorithm collection              dataset_name  AVERAGE_PRECISION_diff  \\\n",
       "0         ARIMA   GutenTAG       cbf-combined-diff-1                0.454742   \n",
       "1         ARIMA   GutenTAG       cbf-combined-diff-2                0.068871   \n",
       "2         ARIMA   GutenTAG       cbf-combined-diff-3                0.241877   \n",
       "3         ARIMA   GutenTAG          cbf-diff-count-1                0.014368   \n",
       "4         ARIMA   GutenTAG          cbf-diff-count-2                0.011311   \n",
       "...         ...        ...                       ...                     ...   \n",
       "10356   k-Means   GutenTAG  sinus-type-pattern-shift               -0.032091   \n",
       "10357   k-Means   GutenTAG        sinus-type-pattern                0.999901   \n",
       "10358   k-Means   GutenTAG       sinus-type-platform               -0.027792   \n",
       "10359   k-Means   GutenTAG          sinus-type-trend                0.000000   \n",
       "10360   k-Means   GutenTAG       sinus-type-variance                0.999019   \n",
       "\n",
       "       PR_AUC_diff  RANGE_PR_AUC_diff  ROC_AUC_diff  train_main_time_diff  \\\n",
       "0         0.465248           0.363823      0.283843                   0.0   \n",
       "1         0.061556           0.010938      0.027273                   0.0   \n",
       "2         0.127965           0.135276      0.741909                   0.0   \n",
       "3         0.008516           0.011273      0.325859                   0.0   \n",
       "4         0.007510           0.000447     -0.163783                   0.0   \n",
       "...            ...                ...           ...                   ...   \n",
       "10356    -0.032544          -0.008014     -0.000149                   0.0   \n",
       "10357     0.999900          -0.000035     -0.000001                   0.0   \n",
       "10358    -0.024494           0.086535     -0.000753                   0.0   \n",
       "10359     0.000000           0.000043      0.000000                   0.0   \n",
       "10360     0.999014           0.000902      0.000009                   0.0   \n",
       "\n",
       "       execute_main_time_diff  \n",
       "0                  -75.097020  \n",
       "1                  -30.884145  \n",
       "2                  -80.336789  \n",
       "3                  -43.147819  \n",
       "4                  -40.103166  \n",
       "...                       ...  \n",
       "10356               45.251641  \n",
       "10357               50.553484  \n",
       "10358               32.716808  \n",
       "10359               16.612107  \n",
       "10360               61.290748  \n",
       "\n",
       "[10361 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_columns = [\"algorithm\", \"collection\", \"dataset_name\"]\n",
    "metric_columns = [\"AVERAGE_PRECISION\", \"PR_AUC\", \"RANGE_PR_AUC\", \"ROC_AUC\", \"train_main_time\", \"execute_main_time\"]\n",
    "columns = key_columns + metric_columns\n",
    "df = pd.merge(df_ref[columns], df_optim[columns], how=\"inner\", on=key_columns, suffixes=(\"_ref\", \"_optim\"))\n",
    "df.fillna(0, inplace=True)\n",
    "\n",
    "for mc in metric_columns:\n",
    "    df[f\"{mc}_diff\"] = df[f\"{mc}_optim\"] - df[f\"{mc}_ref\"]\n",
    "    df.drop(columns=[f\"{mc}_ref\", f\"{mc}_optim\"], inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "native-connectivity",
   "metadata": {},
   "source": [
    "#### Overall algorithm performance change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "indonesian-button",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"6\" halign=\"left\">median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AVERAGE_PRECISION_diff</th>\n",
       "      <th>PR_AUC_diff</th>\n",
       "      <th>RANGE_PR_AUC_diff</th>\n",
       "      <th>ROC_AUC_diff</th>\n",
       "      <th>execute_main_time_diff</th>\n",
       "      <th>train_main_time_diff</th>\n",
       "      <th>AVERAGE_PRECISION_diff</th>\n",
       "      <th>PR_AUC_diff</th>\n",
       "      <th>RANGE_PR_AUC_diff</th>\n",
       "      <th>ROC_AUC_diff</th>\n",
       "      <th>execute_main_time_diff</th>\n",
       "      <th>train_main_time_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NormA</th>\n",
       "      <td>0.421635</td>\n",
       "      <td>0.421323</td>\n",
       "      <td>0.367889</td>\n",
       "      <td>0.265064</td>\n",
       "      <td>2.085377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.179740</td>\n",
       "      <td>0.177563</td>\n",
       "      <td>0.235541</td>\n",
       "      <td>0.310973</td>\n",
       "      <td>1.248615</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALMOD</th>\n",
       "      <td>0.467986</td>\n",
       "      <td>0.466987</td>\n",
       "      <td>0.011349</td>\n",
       "      <td>0.165910</td>\n",
       "      <td>31.401900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.455776</td>\n",
       "      <td>0.455548</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>35.510810</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhaseSpace-SVM</th>\n",
       "      <td>0.497796</td>\n",
       "      <td>0.512652</td>\n",
       "      <td>0.187967</td>\n",
       "      <td>0.164920</td>\n",
       "      <td>16.173163</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.578823</td>\n",
       "      <td>0.578764</td>\n",
       "      <td>0.142633</td>\n",
       "      <td>0.121838</td>\n",
       "      <td>-5.222175</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOT SAX</th>\n",
       "      <td>0.205895</td>\n",
       "      <td>0.207606</td>\n",
       "      <td>0.113128</td>\n",
       "      <td>0.135798</td>\n",
       "      <td>-68.288820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031202</td>\n",
       "      <td>0.020028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARIMA</th>\n",
       "      <td>0.348697</td>\n",
       "      <td>0.353508</td>\n",
       "      <td>0.150179</td>\n",
       "      <td>0.127298</td>\n",
       "      <td>20.188411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.266359</td>\n",
       "      <td>0.263341</td>\n",
       "      <td>0.046071</td>\n",
       "      <td>0.105241</td>\n",
       "      <td>-92.444743</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean                                \\\n",
       "               AVERAGE_PRECISION_diff PR_AUC_diff RANGE_PR_AUC_diff   \n",
       "algorithm                                                             \n",
       "NormA                        0.421635    0.421323          0.367889   \n",
       "VALMOD                       0.467986    0.466987          0.011349   \n",
       "PhaseSpace-SVM               0.497796    0.512652          0.187967   \n",
       "HOT SAX                      0.205895    0.207606          0.113128   \n",
       "ARIMA                        0.348697    0.353508          0.150179   \n",
       "\n",
       "                                                                         \\\n",
       "               ROC_AUC_diff execute_main_time_diff train_main_time_diff   \n",
       "algorithm                                                                 \n",
       "NormA              0.265064               2.085377                  0.0   \n",
       "VALMOD             0.165910              31.401900                  0.0   \n",
       "PhaseSpace-SVM     0.164920              16.173163                  0.0   \n",
       "HOT SAX            0.135798             -68.288820                  0.0   \n",
       "ARIMA              0.127298              20.188411                  0.0   \n",
       "\n",
       "                               median                                \\\n",
       "               AVERAGE_PRECISION_diff PR_AUC_diff RANGE_PR_AUC_diff   \n",
       "algorithm                                                             \n",
       "NormA                        0.179740    0.177563          0.235541   \n",
       "VALMOD                       0.455776    0.455548          0.000002   \n",
       "PhaseSpace-SVM               0.578823    0.578764          0.142633   \n",
       "HOT SAX                      0.031202    0.020028          0.000000   \n",
       "ARIMA                        0.266359    0.263341          0.046071   \n",
       "\n",
       "                                                                         \n",
       "               ROC_AUC_diff execute_main_time_diff train_main_time_diff  \n",
       "algorithm                                                                \n",
       "NormA              0.310973               1.248615                  0.0  \n",
       "VALMOD             0.000003              35.510810                  0.0  \n",
       "PhaseSpace-SVM     0.121838              -5.222175                  0.0  \n",
       "HOT SAX            0.048124               0.000000                  0.0  \n",
       "ARIMA              0.105241             -92.444743                  0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregations = [\"mean\", \"median\"]\n",
    "df_overall_scores = df.pivot_table(index=\"algorithm\", values=[f\"{m}_diff\" for m in metric_columns], aggfunc=aggregations)\n",
    "df_overall_scores = df_overall_scores.sort_values(by=(\"mean\", \"ROC_AUC_diff\"), ascending=False)\n",
    "\n",
    "df_overall_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119a5520",
   "metadata": {},
   "source": [
    "#### AUC_ROC change per algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "neutral-princeton",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asl = df.pivot(index=\"algorithm\", columns=\"dataset_name\", values=\"ROC_AUC_diff\")\n",
    "df_asl = df_asl.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_asl[\"median\"] = df_asl.median(axis=1)\n",
    "df_asl = df_asl.sort_values(by=\"median\", ascending=True)\n",
    "df_asl = df_asl.drop(columns=\"median\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "national-attribute",
   "metadata": {},
   "outputs": [],
   "source": [
    "if default_use_plotly:\n",
    "    fig = go.Figure()\n",
    "    for c in df_asl.columns:\n",
    "        fig.add_trace(go.Violin(\n",
    "            y=df_asl[c],\n",
    "            name=c\n",
    "        ))\n",
    "    fig.update_traces(meanline_visible=True, box_visible=True)\n",
    "    fig.update_layout(\n",
    "        title={\"text\":\"Change in AUC_ROC\", \"xanchor\": \"center\", \"x\": 0.5},\n",
    "        yaxis_title=\"AUC_ROC score\",\n",
    "        legend_title=\"Algorithms\",\n",
    "        violingap=0\n",
    "    )\n",
    "    py.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "popular-question",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_show = 20\n",
    "n_show = n_show // 2\n",
    "\n",
    "if default_use_plotly:\n",
    "    fig = go.Figure()\n",
    "    for i, c in enumerate(df_asl.columns):\n",
    "        fig.add_trace(go.Box(\n",
    "            x=df_asl[c],\n",
    "            name=c,\n",
    "            boxpoints=False,\n",
    "            visible=None if i < n_show or i > len(df_asl.columns)-n_show-1 else \"legendonly\"\n",
    "        ))\n",
    "    fig.update_layout(\n",
    "        title={\"text\":\"AUC_ROC box plots\", \"xanchor\": \"center\", \"x\": 0.5},\n",
    "        xaxis_title=\"AUC_ROC score diff\",\n",
    "        legend_title=\"Algorithms\"\n",
    "    )\n",
    "    py.iplot(fig)\n",
    "else:\n",
    "    df_tmp = df_asl.fillna(0)\n",
    "    df_tmp = df_tmp[list(df_tmp.columns)[:n_show]+list(df_tmp.columns)[-n_show:]]\n",
    "    plt.boxplot(df_tmp, vert=True, labels=df_tmp.columns, meanline=True, showmeans=True, showfliers=False, manage_ticks=True)\n",
    "    plt.xticks(rotation=-45, ha=\"left\", rotation_mode=\"anchor\")\n",
    "    plt.title(\"AUC_ROC score diff\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b3c16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_32339/4050411280.py:40: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bars(df, columns=[\"ROC_AUC_diff\", \"RANGE_PR_AUC_diff\"], title=\"Quality change\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac3b0b0",
   "metadata": {},
   "source": [
    "#### Look into algorithms whose performance degraded\n",
    "\n",
    "- Hybrid KNN --> **rerun**\n",
    "- DBStream --> **rerun**\n",
    "- Bagel --> **rerun**\n",
    "- LSTM-AD --> **rerun**\n",
    "- EncDec-AD --> keep\n",
    "- MultiHMM --> **rerun**\n",
    "\n",
    "Errors and timeouts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa4d14e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_parameters(algo):\n",
    "    print(f\"Parameters of {algo}:\")\n",
    "    ref_params = df_ref[df_ref[\"algorithm\"] == algo][\"hyper_params\"].unique()\n",
    "    print(\"\\n### Ref:\")\n",
    "    param_d = {}\n",
    "    for param_set in ref_params:\n",
    "        param_set = json.loads(param_set)\n",
    "        for param in param_set:\n",
    "            if param not in param_d:\n",
    "                param_d[param] = []\n",
    "            param_d[param].append(param_set[param])\n",
    "    for param in sorted(param_d):\n",
    "        print(f\"  {param}: {np.unique(param_d[param])}\")\n",
    "\n",
    "    optim_params = df_optim[df_optim[\"algorithm\"] == algo][\"hyper_params\"].unique()\n",
    "    print(\"\\n### Optim:\")\n",
    "    param_d = {}\n",
    "    for param_set in optim_params:\n",
    "        param_set = json.loads(param_set)\n",
    "        for param in param_set:\n",
    "            if param not in param_d:\n",
    "                param_d[param] = []\n",
    "            param_d[param].append(param_set[param])\n",
    "    for param in sorted(param_d):\n",
    "        print(f\"  {param}: {np.unique(param_d[param])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea7db08e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>algo</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Hybrid KNN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">DBStream</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bagel</th>\n",
       "      <th colspan=\"2\" halign=\"left\">LSTM-AD</th>\n",
       "      <th colspan=\"2\" halign=\"left\">EncDec-AD</th>\n",
       "      <th colspan=\"2\" halign=\"left\">MultiHMM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp</th>\n",
       "      <th>ref</th>\n",
       "      <th>optim</th>\n",
       "      <th>ref</th>\n",
       "      <th>optim</th>\n",
       "      <th>ref</th>\n",
       "      <th>optim</th>\n",
       "      <th>ref</th>\n",
       "      <th>optim</th>\n",
       "      <th>ref</th>\n",
       "      <th>optim</th>\n",
       "      <th>ref</th>\n",
       "      <th>optim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Status.OK</th>\n",
       "      <td>193.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.ERROR</th>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "algo           Hybrid KNN        DBStream         Bagel        LSTM-AD         \\\n",
       "exp                   ref  optim      ref  optim    ref  optim     ref  optim   \n",
       "Status.OK           193.0   12.0    169.0   22.0  168.0    0.0   193.0   75.0   \n",
       "Status.ERROR          0.0  181.0     24.0  171.0    0.0  168.0     0.0  111.0   \n",
       "Status.TIMEOUT        0.0    0.0      0.0    0.0    0.0    0.0     0.0    7.0   \n",
       "\n",
       "algo           EncDec-AD        MultiHMM         \n",
       "exp                  ref  optim      ref  optim  \n",
       "Status.OK          134.0   17.0    150.0   81.0  \n",
       "Status.ERROR        49.0   39.0     43.0  112.0  \n",
       "Status.TIMEOUT      10.0  137.0      0.0    0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_list = [\"Hybrid KNN\", \"DBStream\", \"Bagel\", \"LSTM-AD\", \"EncDec-AD\", \"MultiHMM\"]\n",
    "\n",
    "df_tmp = pd.DataFrame(columns=[\"Status.OK\", \"Status.ERROR\", \"Status.TIMEOUT\"], index=pd.MultiIndex.from_product([algo_list, [\"ref\", \"optim\"]], names=[\"algo\", \"exp\"]))\n",
    "for algo in algo_list:\n",
    "    df_tmp.loc[(algo, \"ref\"), :] = df_ref[df_ref[\"algorithm\"] == algo].groupby(by=[\"status\"])[\"repetition\"].count()\n",
    "    df_tmp.loc[(algo, \"optim\"), :] = df_optim[df_optim[\"algorithm\"] == algo].groupby(by=[\"status\"])[\"repetition\"].count()\n",
    "df_tmp.fillna(0, inplace=True)\n",
    "df_tmp.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c63946",
   "metadata": {},
   "source": [
    "##### Hybrid KNN\n",
    "\n",
    "Error message:\n",
    "\n",
    "```\n",
    "/usr/local/lib/python3.7/site-packages/torch/autograd/__init__.py:132: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
    "  allow_unreachable=True)  # allow_unreachable flag\n",
    "Traceback (most recent call last):\n",
    "  File \"/app/algorithm.py\", line 79, in <module>\n",
    "    train(args)\n",
    "  File \"/app/algorithm.py\", line 55, in train\n",
    "    model.fit(data, args.modelOutput)\n",
    "  File \"/app/hybrid_knn/hybrid_knn.py\", line 39, in fit\n",
    "    self.dae.fit(X, callbacks=[knn_callback, save_callback])\n",
    "  File \"/app/hybrid_knn/dae/dae.py\", line 113, in fit\n",
    "    early_stopping.update(validation_loss)\n",
    "  File \"/app/hybrid_knn/dae/early_stopping.py\", line 28, in update\n",
    "    self._callback(improvement, loss)\n",
    "  File \"/app/hybrid_knn/dae/early_stopping.py\", line 17, in _callback\n",
    "    cb(improvement, loss, self.epochs_without_change)\n",
    "  File \"/app/hybrid_knn/hybrid_knn.py\", line 33, in knn_callback\n",
    "    self.knn.fit(self.dae.reduce(X))\n",
    "  File \"/app/hybrid_knn/knn/knn.py\", line 20, in fit\n",
    "    self.g = [self._calculate_kth_distance(s) for s in self.s]\n",
    "  File \"/app/hybrid_knn/knn/knn.py\", line 20, in <listcomp>\n",
    "    self.g = [self._calculate_kth_distance(s) for s in self.s]\n",
    "  File \"/app/hybrid_knn/knn/knn.py\", line 27, in _calculate_kth_distance\n",
    "    d[:, l] = knn.kth_neighbor_distance(X)\n",
    "  File \"/app/hybrid_knn/knn/knn.py\", line 50, in kth_neighbor_distance\n",
    "    distances, _ = self.nbrs.kneighbors(X, self.k, return_distance=True)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/neighbors/_base.py\", line 683, in kneighbors\n",
    "    (n_samples_fit, n_neighbors)\n",
    "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 9, n_neighbors = 10\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "108523e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters of Hybrid KNN:\n",
      "\n",
      "### Ref:\n",
      "  anomaly_window_size: [   1   10   50  100  200  500 1000]\n",
      "  batch_size: [64]\n",
      "  early_stopping_delta: [0.05]\n",
      "  early_stopping_patience: [10]\n",
      "  epochs: [500]\n",
      "  learning_rate: [0.001]\n",
      "  random_state: [42]\n",
      "  split: [0.8]\n",
      "  test_batch_size: [64]\n",
      "\n",
      "### Optim:\n",
      "  anomaly_window_size: [   1   10   50  100  200  500 1000]\n",
      "  batch_size: [64]\n",
      "  early_stopping_delta: [0.05]\n",
      "  early_stopping_patience: [10]\n",
      "  epochs: [500]\n",
      "  learning_rate: [0.001]\n",
      "  n_estimators: [1000]\n",
      "  n_neighbors: [10]\n",
      "  random_state: [42]\n",
      "  split: [0.8]\n",
      "  test_batch_size: [64]\n"
     ]
    }
   ],
   "source": [
    "list_parameters(\"Hybrid KNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc24d6f",
   "metadata": {},
   "source": [
    "--> Parameter configuration error: Combination of `n_estimators=1000` and `n_neighbors=10` is too large for the number of samples in our datasets.\n",
    "\n",
    "--> **reduced to `n_estimators=500` and `n_neighbors=10`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4819936",
   "metadata": {},
   "source": [
    "##### DBStream\n",
    "\n",
    "Error message:\n",
    "\n",
    "```\n",
    "Error in cutree(hc, k = k) : elements of 'k' must be between 1 and 33\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51be7973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters of DBStream:\n",
      "\n",
      "### Ref:\n",
      "  distance_metric: ['euclidean']\n",
      "  random_state: [42]\n",
      "  window_size: [ 12  22  30  64 150]\n",
      "\n",
      "### Optim:\n",
      "  alpha: [0.5]\n",
      "  distance_metric: ['euclidean']\n",
      "  lambda: [0.001]\n",
      "  min_weight: [0]\n",
      "  n_clusters: [50]\n",
      "  radius: [1.3]\n",
      "  random_state: [42]\n",
      "  shared_density: [ True]\n",
      "  window_size: [  8  15  20  43 100]\n"
     ]
    }
   ],
   "source": [
    "list_parameters(\"DBStream\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead07409",
   "metadata": {},
   "source": [
    "--> Parameter configuration error: `n_clusters` must be below 33 and `50` is too large!\n",
    "\n",
    "--> **reduced to `n_clusters=30`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73513df",
   "metadata": {},
   "source": [
    "##### Bagel\n",
    "\n",
    "Error message:\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"/app/algorithm.py\", line 117, in <module>\n",
    "    train(args, kpi)\n",
    "  File \"/app/algorithm.py\", line 84, in train\n",
    "    early_stopping_patience=cp.early_stopping_patience\n",
    "  File \"/app/bagel/model.py\", line 104, in __init__\n",
    "    self.window_size + self.condition_size, self.latent_dims, self.network_size, eps=1e-4),\n",
    "  File \"/app/bagel/network/fc_gaussian_statistic.py\", line 26, in __init__\n",
    "    for current_size in net_size:\n",
    "TypeError: 'int' object is not iterable\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3bd1a43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters of Bagel:\n",
      "\n",
      "### Ref:\n",
      "  cuda: [False]\n",
      "  early_stopping_delta: [0.05]\n",
      "  early_stopping_patience: [10]\n",
      "  epochs: [500]\n",
      "  random_state: [42]\n",
      "  split: [0.8]\n",
      "\n",
      "### Optim:\n",
      "  batch_size: [64]\n",
      "  cuda: [False]\n",
      "  dropout: [0.1]\n",
      "  early_stopping_delta: [0.05]\n",
      "  early_stopping_patience: [10]\n",
      "  epochs: [500]\n",
      "  hidden_layer_shape: [100]\n",
      "  latent_size: [6]\n",
      "  random_state: [42]\n",
      "  split: [0.8]\n",
      "  window_size: [ 16  30  40  86 200]\n"
     ]
    }
   ],
   "source": [
    "list_parameters(\"Bagel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7506b7",
   "metadata": {},
   "source": [
    "--> `AlgorithmConfigurator` was assuming that `hidden_layer_shape: [100, 100]` is a search space instead of a single parameter value. This is also the reason, why there are duplicates!\n",
    "\n",
    "--> **fixed `AlgorithmConfigurator`!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e65c57d",
   "metadata": {},
   "source": [
    "##### LSTM-AD\n",
    "\n",
    "OOM (`status code '137'`) for most datasets, but for multivariate ones, we also get:\n",
    "\n",
    "```\n",
    "/usr/local/lib/python3.7/site-packages/torch/autograd/__init__.py:132: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
    "  allow_unreachable=True)  # allow_unreachable flag\n",
    "Traceback (most recent call last):\n",
    "  File \"/app/algorithm.py\", line 76, in <module>\n",
    "    train(args)\n",
    "  File \"/app/algorithm.py\", line 52, in train\n",
    "    model.fit(data, args.modelOutput)\n",
    "  File \"/app/lstm_ad/model.py\", line 125, in fit\n",
    "    early_stopping.update(validation_loss)\n",
    "  File \"/app/lstm_ad/early_stopping.py\", line 28, in update\n",
    "    self._callback(improvement, loss)\n",
    "  File \"/app/lstm_ad/early_stopping.py\", line 17, in _callback\n",
    "    cb(improvement, loss, self.epochs_without_change)\n",
    "  File \"/app/lstm_ad/model.py\", line 104, in cb\n",
    "    self._estimate_normal_distribution(valid_dl)\n",
    "  File \"/app/lstm_ad/model.py\", line 138, in _estimate_normal_distribution\n",
    "    e = torch.abs(y[:, -1, :] - y_hat)\n",
    "RuntimeError: The size of tensor a (3) must match the size of tensor b (150) at non-singleton dimension 1\n",
    "```\n",
    "\n",
    "Exemplary datasets with this error:\n",
    "\n",
    "- sinus-channels-all-of-3.semi-supervised\n",
    "- cbf-channels-all-of-3.semi-supervised\n",
    "- ecg-channels-single-of-5.semi-supervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0b2fd64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters of LSTM-AD:\n",
      "\n",
      "### Ref:\n",
      "  batch_size: [64]\n",
      "  early_stopping_delta: [0.05]\n",
      "  early_stopping_patience: [10]\n",
      "  epochs: [500]\n",
      "  learning_rate: [0.001]\n",
      "  random_state: [42]\n",
      "  split: [0.8]\n",
      "  test_batch_size: [64]\n",
      "  validation_batch_size: [64]\n",
      "  window_size: [ 12  22  30  64 150]\n",
      "\n",
      "### Optim:\n",
      "  batch_size: [64]\n",
      "  early_stopping_delta: [0.05]\n",
      "  early_stopping_patience: [10]\n",
      "  epochs: [500]\n",
      "  learning_rate: [0.001]\n",
      "  lstm_layers: [1]\n",
      "  prediction_window_size: [50]\n",
      "  random_state: [42]\n",
      "  split: [0.8]\n",
      "  test_batch_size: [64]\n",
      "  validation_batch_size: [64]\n",
      "  window_size: [ 16  30  40  86 200]\n"
     ]
    }
   ],
   "source": [
    "list_parameters(\"LSTM-AD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546d67a9",
   "metadata": {},
   "source": [
    "--> **OOM** error is unfortunate, but nothing we can change!\n",
    "\n",
    "--> the shape error on multivariate datasets seems to be due to a change of parameter `prediction_window_size` from `1` to `50`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10acb16",
   "metadata": {},
   "source": [
    "##### EncDec-AD\n",
    "\n",
    "--> Reduced performance due to a lot of **timeout**s. Errors decreased.\n",
    "\n",
    "--> **no change necessary**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b1e073",
   "metadata": {},
   "source": [
    "##### MultiHMM\n",
    "\n",
    "87 error messages:\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"/app/algorithm.py\", line 73, in <module>\n",
    "    execute(args)\n",
    "  File \"/app/algorithm.py\", line 54, in execute\n",
    "    model = joblib.load(args.modelInput)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/joblib/numpy_pickle.py\", line 585, in load\n",
    "    obj = _unpickle(fobj, filename, mmap_mode)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/joblib/numpy_pickle.py\", line 504, in _unpickle\n",
    "    obj = unpickler.load()\n",
    "  File \"/usr/local/lib/python3.7/pickle.py\", line 1088, in load\n",
    "    dispatch[key[0]](self)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/joblib/numpy_pickle.py\", line 329, in load_build\n",
    "    Unpickler.load_build(self)\n",
    "  File \"/usr/local/lib/python3.7/pickle.py\", line 1552, in load_build\n",
    "    setstate(state)\n",
    "  File \"/app/multi_hmm/model.py\", line 35, in __setstate__\n",
    "    self.hmm = pg.HiddenMarkovModel.from_json(self.hmm)\n",
    "  File \"pomegranate/base.pyx\", line 97, in pomegranate.base.Model.from_json\n",
    "  File \"pomegranate/hmm.pyx\", line 3264, in pomegranate.hmm.HiddenMarkovModel.from_dict\n",
    "  File \"pomegranate/base.pyx\", line 504, in pomegranate.base.State.from_dict\n",
    "  File \"pomegranate/distributions/distributions.pyx\", line 303, in pomegranate.distributions.distributions.Distribution.from_dict\n",
    "  File \"<string>\", line 1, in <module>\n",
    "NameError: name 'nan' is not defined\n",
    "```\n",
    "\n",
    "25 error messages (on multivariate datasets):\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"/app/algorithm.py\", line 71, in <module>\n",
    "    train(args)\n",
    "  File \"/app/algorithm.py\", line 48, in train\n",
    "    model.fit(data, labels)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/pipeline.py\", line 341, in fit\n",
    "    Xt = self._fit(X, y, **fit_params_steps)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/pipeline.py\", line 307, in _fit\n",
    "    **fit_params_steps[name])\n",
    "  File \"/usr/local/lib/python3.7/site-packages/joblib/memory.py\", line 352, in __call__\n",
    "    return self.func(*args, **kwargs)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\n",
    "    res = transformer.fit_transform(X, y, **fit_params)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/pipeline.py\", line 387, in fit_transform\n",
    "    return last_step.fit_transform(Xt, y, **fit_params_last_step)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/base.py\", line 702, in fit_transform\n",
    "    return self.fit(X, y, **fit_params).transform(X)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/_discretization.py\", line 153, in fit\n",
    "    X = self._validate_data(X, dtype='numeric')\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/base.py\", line 421, in _validate_data\n",
    "    X = check_array(X, **check_params)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py\", line 63, in inner_f\n",
    "    return f(*args, **kwargs)\n",
    "  File \"/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py\", line 698, in check_array\n",
    "    \"if it contains a single sample.\".format(array))\n",
    "ValueError: Expected 2D array, got 1D array instead:\n",
    "array=[-0.11283424  1.73056285  3.66144375 ... -0.25435671 -0.27262701\n",
    " -0.27213188].\n",
    "Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc531cf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters of MultiHMM:\n",
      "\n",
      "### Ref:\n",
      "  random_state: [42]\n",
      "\n",
      "### Optim:\n",
      "  discretizer: ['choquet']\n",
      "  n_bins: [5]\n",
      "  random_state: [42]\n"
     ]
    }
   ],
   "source": [
    "list_parameters(\"MultiHMM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cced57ed",
   "metadata": {},
   "source": [
    "--> **fixed multivariate dataset error**\n",
    "\n",
    "--> **won't fix nan-error** (issue ref: [akita/timeeval-algorithms#45](https://gitlab.hpi.de/akita/timeeval-algorithms/-/issues/45))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2348c9b",
   "metadata": {},
   "source": [
    "#### Runtime change per algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7517d296",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_32339/4050411280.py:40: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ffpm3v/3tTJ06lalTp/KDH/ygaV5nzJjBQQcdxA477MCYMWP42te+xvHHH8/EiRPZaaed+Pe//w1EAGzq1KlMmDCBww47jBjcBwcffDA333zzG/k77bTT2GCDDZg4cSLPPvtsw+9slOcZM2bw2c9+FoBp06ZxzDHHsNVWW7Huuuvy6KOPstdee7Hmmmty8sknN13P//nPf5r+zi9+8YustdZabL311n3WR/m93/zmNzn33HO58sor2WabbZg+fTq/+tWv2H333TnnnHNab3BJkiRJkrRA6UpgLKU0lAiKXVcUxdfy4v/Ol0eS//4xL38JGF35+Cjgd3n5qAbLF0jPPPMMX/3qV/nBD37AE088weDBg3nuuefYfffdOfnkkzn++ON597vfzYQJEzjzzDPZdtttefTRR7nvvvs47rjj+Oc//8nll1/OCy+8wOOPP85Pf/rTlpfyjRkzhunTp3PMMcfwxBNPsOWWW3L00UdzzDHH8Oijj3LLLbdw6KGHtszzL3/5S+68805uu+023v3ud7PNNtvw5JNPMmLECO68804AjjrqKB599FGeeuop/vWvf3HHHXc0TGvZZZflxz/+MYcffvgbga5O8lxvoYUW4vvf/z7Tp09njz324KKLLuKpp57i6quv5s9//nPD9Xzdddc1/L7f//73nHbaafzgBz/g7rvv5umnn57lPbvssssbebrvvvu49NJLWWmllbjvvvs45phjWq4/SZIkSZK0YOn3pZT5zpFfAJ4piuLzlZduBw4Czsp/b6ssvz6l9Hli8v01gUeKovhPSukfKaVNiEsxDwQu6G/+Bsq9997LY489xtSpUwH417/+xXLLLcepp57K1KlTGT58OOeffz4A3/nOd7j99tvfCCC9+uqr/OY3v+Gee+5h+vTpDBkSm2nppZeerTzcc889fYI/f//73/nHP/7BYost1vD9O++8M0OHDmXixIn85z//YaeddgJg4sSJvPjiiwDcd999fOYzn+F///d/+ctf/sL48ePZbbfdZklrr732AmDDDTfka1/72iyvd2r33Xd/Iw/jx49nxRVXBGC11Vbjt7/9LQ8++GDD9dzIww8/zLRp0yhHGb7zne/k5z//+RznTZIkSZIkLdi6McfY5sABwJMppSfyspOIgNiNKaX3Ar8B9gEoiuJnKaUbgaeJO1oeme9ICXA4cDUwgrgbZf/vSDlAiqLgoIMO4lOf+lSf5X/4wx/4n//5H/7973/z6quvssgii1AUBbfccgtrr732LGlE3LGvIUOG8PrrrwMRRGvm9ddf56GHHmLEiBEd5XnYsGEADBo0iKFDh77x3YMGDeK1117j1Vdf5YgjjmDmzJmMHj2aGTNmNP3+Mq3Bgwfz2muvdfT97fJU/l/NU7P13Eyj9SlJkiRJknpTvwNjRVE8SOP5wQC2a/KZM4EzGyyfCUzob55mMeOVrifZznbbbccee+zBMcccw3LLLcdf/vIX/vGPf/CBD3yAT3ziE7zwwgt89KMf5cILL2THHXfkggsu4IILLiClxOOPP87666/PDjvswKWXXsq0adMYMmQIf/nLX1h66aUZM2YMjz32GDvvvDO33HLLG9+52GKL8fe/16Z322GHHbjwwgs57rjjAHjiiSeYPHnyHP+mMgi27LLL8j//8z/cfPPN7L333nOcXqM8z65m63nVVVed5b0bb7wxRx99NH/+859ZfPHFuemmm1hvvfX6k31JkiRJkrQA69rk++pr3LhxnHHGGeywww5MmjSJt7zlLVxzzTUMGTKEd73rXZxwwgk8+uijfPe73+WUU07h3//+N5MmTWLChAmccsopABx66KGsssoqTJo0ifXWW4/rr78egNNOO42jjz6aLbfcksGDB7/xnbvtthu33nrrGxPZn3/++cycOZNJkyYxbtw4Lr300n79piWXXJL3ve99TJw4kT333PONyxf7oz7Ps6vRev7973/f8L0rrrgiM2bMYNNNN2X77bdngw026G/2JUmSJEnSAiyVdxVcUE2ZMqWYOXNmn2XPPPMM66677gDlSL3EfU2SJEmS3vzGnHDnLMtePGvXAcjJvJNSeqwoiikDnY+5zRFjkiRJkiRJ6kndmHxfC5AvfvGLnHfeeX2Wbb755lx00UVvqu/deOON+b//+78+y770pS8xceLEufJ9kiRJkiRpwfOmDYw1u6NjrzvkkEM45JBD3vTf+/DDD8/171jQL0OWJEmSJKnXvSkvpRw+fDh//vOfDVxorimKgj//+c8MHz58oLMiSZIkSZLm0JtyxNioUaN46aWXePnllwc6K3oTGz58OKNGjRrobEiSJEmSpDn0pgyMDR06lLFjxw50NiRJkiRJkjQfe1NeSilJkiRJkiS1Y2BMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPWnIQGdAkiRJkiTNx2Ys0WDZK/M+H9Jc4IgxSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJQwY6A5IkSZIkSXPDmBPunGXZi2ftOgA50fzKEWOSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSelJXAmMppatSSn9MKT1VWbZ0SunulNIv8t+lKq+dmFJ6PqX0XEppx8ryDVNKT+bXzk8ppW7kT5IkSZIkSarXrRFjVwM71S07Abi3KIo1gXvzc1JK44B9gfH5MxenlAbnz1wCHAasmR/1aUqSJEmSJEld0ZXAWFEU3wf+Urd4D+Ca/P81wJ6V5TcURfF/RVG8ADwPbJRSWhFYvCiKh4qiKIBrK5+RJEmSJEmSumpuzjG2fFEUvwfIf5fLy1cGflt530t52cr5//rls0gpHZZSmplSmvnyyy93PeOSJEmSJEl68xuIyfcbzRtWtFg+68KiuLwoiilFUUwZOXJkVzMnSZIkSZKk3jA3A2P/nS+PJP/9Y17+EjC68r5RwO/y8lENlkuSJEmSJEldNzcDY7cDB+X/DwJuqyzfN6U0LKU0lphk/5F8ueU/Ukqb5LtRHlj5jCRJkiRJktRVQ7qRSErpK8A0YNmU0kvAacBZwI0ppfcCvwH2ASiK4mcppRuBp4HXgCOLovhPTupw4g6XI4C78kOSJEmSJEnquq4Exoqi2K/JS9s1ef+ZwJkNls8EJnQjT5IkSZIkSVIrAzH5viRJkiRJkjTgDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8aMtAZkCRJkiRJqjfmhDtnWfbiWbsOQE70ZuaIMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTxoy0BmQJEmSJElvHmNOuHOWZS+etesA5ERqzxFjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknrSkIHOgCRJXTdjiQbLXpn3+ZAkSZI0X3PEmCRJkiRJknqSI8bU2xxVIkmSJElSzzIwJkmSJEmSGHPCnQ2Xvzh8HmdEmocMjEmSJElSM15hIElvavNdYCyltBNwHjAYuLIoirMGOEuSJEmSJM3XGo32evGsXQcgJ9KCZb4KjKWUBgMXAW8BXgIeTSndXhTF0wObM0nK7DWWNLdZzkhSTzGgJQ2s+SowBmwEPF8Uxa8AUko3AHsABsYkaW6xEd475qdt3Sgv4L43v+nWPjM/7Xuau9zW6iFN5+MyqCUtUFJRFAOdhzeklPYGdiqK4tD8/ABg46Iojqp732HAYQCrrLLKhr/+9a/neV7nhoY9BcPfNesbW1Qumk+W2P905qSA70Y689Nv6tbJb66u3wHaZ+YqK9kLBreTFmRvtv3XwGNzBvskSepISumxoiimDHQ+5rb5bcRYarBslshdURSXA5cDTJkyZf6J7EkLoKYBuRnzNBuSJEmSJM1z81tg7CVgdOX5KOB3A5QXSfMLe+AXDG4nSZIkSQuYQQOdgTqPAmumlMamlBYC9gVuH+A8SZIkSZIk6U1ovhoxVhTFaymlo4BvA4OBq4qi+NkAZ0uSJGnB4ghOSZKkjsxXgTGAoii+CXxzoPMhSZIkNWXwUZKkN4X5LjAmSZIkzTUGtCRJUoWBMWkB1vCOkjPmeTYkSZIkSVogzW+T70uSJEmSJEnzhIExSZIkSZIk9SQDY5IkSZIkSepJzjEmDYCGc4NJkiRJkqR5yhFjkiRJkiRJ6kmOGJMkSZrxykDnQJIkSQPAEWOSJEmSJEnqSY4Yk2aDc4NJkiRJkvTm4YgxSZIkSZIk9SRHjKlnONpLkiRJkiRVOWJMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8aMtAZkNRlM14Z6BxIkiRJkrRAcMSYJEmSJEmSepKBMUmSJEmSJPUkL6XUfO/Fs3Zt8oqXDEqSJEmSpDnniDFJkiRJkiT1JEeMaa5qPtpLkiRJkiRpYDliTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJTr4vqbEZrwx0DiRJkiRJmqsMjKkh7yYpSZIkSZLe7LyUUpIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST1pyEBnQG3MeGWgcyBJkiRJkvSm5IgxSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUk5xj7E3mxbN2bfzCjHmaDUmSJEmSpPmeI8YkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJ/QqMpZT2SSn9LKX0ekppSt1rJ6aUnk8pPZdS2rGyfMOU0pP5tfNTSikvH5ZS+mpe/nBKaUx/8iZJkiRJkiS10t8RY08BewHfry5MKY0D9gXGAzsBF6eUBueXLwEOA9bMj53y8vcCfy2KYg3gHODT/cybJEmSJEmS1FS/AmNFUTxTFMVzDV7aA7ihKIr/K4riBeB5YKOU0orA4kVRPFQURQFcC+xZ+cw1+f+bge3K0WSSJEmSJElSt82tOcZWBn5bef5SXrZy/r9+eZ/PFEXxGvAKsEyjxFNKh6WUZqaUZr788stdzrokSZIkSZJ6wZB2b0gp3QOs0OCljxVFcVuzjzVYVrRY3uozsy4sisuBywGmTJnS8D2qM+OVgc6BJEmSJEnSfKVtYKwoiu3nIN2XgNGV56OA3+Xloxosr37mpZTSEGAJ4C9z8N2SJEmSJElSW3PrUsrbgX3znSbHEpPsP1IUxe+Bf6SUNsnzhx0I3Fb5zEH5/72B7+Z5yCRJkiRJkqSuaztirJWU0tuAC4CRwJ0ppSeKotixKIqfpZRuBJ4GXgOOLIriP/ljhwNXAyOAu/ID4AvAl1JKzxMjxfbtT94kSZIkSZKkVvoVGCuK4lbg1iavnQmc2WD5TGBCg+WvAvv0Jz+SJEmSJElSp+bWpZSSJEmSJEnSfM3AmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktST+hUYSymdnVJ6NqX005TSrSmlJSuvnZhSej6l9FxKacfK8g1TSk/m185PKaW8fFhK6at5+cMppTH9yZskSZIkSZLUSn9HjN0NTCiKYhLwc+BEgJTSOGBfYDywE3BxSmlw/swlwGHAmvmxU17+XuCvRVGsAZwDfLqfeZMkSZIkSZKa6ldgrCiK7xRF8Vp++iNgVP5/D+CGoij+ryiKF4DngY1SSisCixdF8VBRFAVwLbBn5TPX5P9vBrYrR5NJkiRJkiRJ3dbNOcbeA9yV/18Z+G3ltZfyspXz//XL+3wmB9teAZZp9EUppcNSSjNTSjNffvnlrv0ASZIkSZIk9Y4h7d6QUroHWKHBSx8riuK2/J6PAa8B15Ufa/D+osXyVp+ZdWFRXA5cDjBlypSG75EkSZIkSZJaaRsYK4pi+1avp5QOAt4KbJcvj4QYCTa68rZRwO/y8lENllc/81JKaQiwBPCXDn6DJEmSJEmSNNv6e1fKnYCPArsXRfG/lZduB/bNd5ocS0yy/0hRFL8H/pFS2iTPH3YgcFvlMwfl//cGvlsJtEmSJEmSJEld1XbEWBsXAsOAu/M8+T8qimJ6URQ/SyndCDxNXGJ5ZFEU/8mfORy4GhhBzElWzkv2BeBLKaXniZFi+/Yzb5IkSZIkSVJT/QqMFUWxRovXzgTObLB8JjChwfJXgX36kx9JkiRJkiSpU928K6UkSZIkSZK0wDAwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSQbGJEmSJEmS1JMMjEmSJEmSJKknGRiTJEmSJElSTzIwJkmSJEmSpJ5kYEySJEmSJEk9ycCYJEmSJEmSepKBMUmSJEmSJPUkA2OSJEmSJEnqSf0KjKWUPpFS+mlK6YmU0ndSSitVXjsxpfR8Sum5lNKOleUbppSezK+dn1JKefmwlNJX8/KHU0pj+pM3SZIkSZIkqZX+jhg7uyiKSUVRTAbuAE4FSCmNA/YFxgM7ARenlAbnz1wCHAasmR875eXvBf5aFMUawDnAp/uZN0mSJEmSJKmpfgXGiqL4e+XpIkCR/98DuKEoiv8riuIF4Hlgo5TSisDiRVE8VBRFAVwL7Fn5zDX5/5uB7crRZJIkSZIkSVK3DelvAimlM4EDgVeAbfLilYEfVd72Ul727/x//fLyM78FKIritZTSK8AywJ8afOdhxKgzVllllf7+BEmSJEmSJPWgtiPGUkr3pJSeavDYA6Aoio8VRTEauA44qvxYg6SKFstbfWbWhUVxeVEUU4qimDJy5Mh2P0GSJEmSJEmaRdsRY0VRbN9hWtcDdwKnESPBRldeGwX8Li8f1WA5lc+8lFIaAiwB/KXD75YkSZIkSZJmS3/vSrlm5enuwLP5/9uBffOdJscSk+w/UhTF74F/pJQ2yfOHHQjcVvnMQfn/vYHv5nnIJEmSJEmSpK7r7xxjZ6WU1gZeB34NTAcoiuJnKaUbgaeB14Aji6L4T/7M4cDVwAjgrvwA+ALwpZTS88RIsX37mTdJkiRJkiSpqX4FxoqieHuL184EzmywfCYwocHyV4F9+pMfSZIkSZIkqVP9upRSkiRJkiRJWlAZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6koExSZIkSZIk9SQDY5IkSZIkSepJBsYkSZIkSZLUkwyMSZIkSZIkqScZGJMkSZIkSVJPMjAmSZIkSZKknmRgTJIkSZIkST3JwJgkSZIkSZJ6UlcCYymlY1NKRUpp2cqyE1NKz6eUnksp7VhZvmFK6cn82vkppZSXD0spfTUvfzilNKYbeZMkSZIkSZIa6XdgLKU0GngL8JvKsnHAvsB4YCfg4pTS4PzyJcBhwJr5sVNe/l7gr0VRrAGcA3y6v3mTJEmSJEmSmunGiLFzgOOBorJsD+CGoij+ryiKF4DngY1SSisCixdF8VBRFAVwLbBn5TPX5P9vBrYrR5NJkiRJkiRJ3davwFhKaXfgv4qi+EndSysDv608fykvWzn/X7+8z2eKongNeAVYpsn3HpZSmplSmvnyyy/35ydIkiRJkiSpRw1p94aU0j3ACg1e+hhwErBDo481WFa0WN7qM7MuLIrLgcsBpkyZ0vA9kiRJkiRJUittA2NFUWzfaHlKaSIwFvhJvuJxFPDjlNJGxEiw0ZW3jwJ+l5eParCcymdeSikNAZYA/jI7P0aSJEmSJEnq1BxfSlkUxZNFUSxXFMWYoijGEIGtDYqi+ANwO7BvvtPkWGKS/UeKovg98I+U0iZ5/rADgdtykrcDB+X/9wa+m+chkyRJkiRJkrqu7YixOVEUxc9SSjcCTwOvAUcWRfGf/PLhwNXACOCu/AD4AvCllNLzxEixfedG3iRJkiRJkiToYmAsjxqrPj8TOLPB+2YCExosfxXYp1v5kSRJkiRJklrp110pJUmSJEmSpAWVgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeNGSgM6CaF8/adaCzIEmSJEmS1DMcMSZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknqSgTFJkiRJkiT1JANjkiRJkiRJ6kkGxiRJkiRJktSTDIxJkiRJkiSpJxkYkyRJkiRJUk8yMCZJkiRJkqSeZGBMkiRJkiRJPcnAmCRJkiRJknpSKopioPPQLymll4FfD3Q+5qJlgT/NJ+nMT3npVjrmZe6mY17mbjrzU166lY55mbvpmJe5m455mbvpzE956VY65mXupmNe5m4681NeupWOeZm76ZiXBc+qRVGMHOhMzG0LfGDszS6lNLMoiinzQzrzU166lY55mbvpmJe5m878lJdupWNe5m465mXupmNe5m4681NeupWOeZm76ZiXuZvO/JSXbqVjXuZuOuZF8ysvpZQkSZIkSVJPMjAmSZIkSZKknmRgbP53+XyUzvyUl26lY17mbjrmZe6mMz/lpVvpmJe5m455mbvpmJe5m878lJdupWNe5m465mXupjM/5aVb6ZiXuZuOedF8yTnGJEmSJEmS1JMcMSZJkiRJkqSeZGBMkiRJkiRJPcnA2AIgpbR0l9LZIqU0bqDTyOkM628a3ZJSWrZL6SzThTQW6kZeuqVb26lb+/D8IKW0ZBfT6tax3e90ulQ+jOhvPtTam+lY6ib3vbkrpZTml3S6mJf5pvztZjrd0KVzSre203xTR5ufdHG9dCWdbujWNupSfbxb63fRLqUzP5UP/V433Sof5ifzWxtOs8/A2HwupbQr8KeU0k79TGdn4PvAdSmltbqQxtr9yMsmwNMppSXmNI2cztSU0nb9TGM3YGZKaUh/CumU0rbAyymlSf1IYz3gFyml0XOaRk5nqZTS8C6s325tp37vw/3d1imlhVNKW6eUDkkpjelHOqsDt3ap0dCtY7sb63cnasf2OnOYxgrAN7sQXOvKealL5UPKx9MK80k63djW/T4W5pc0Kml1a9/r93bqVhpz+tkGafX7OAAoujQhbTfS6UYa81P52610Ukor9ycP3cwLdG07dbuOtmY/87NrSmmzfqYxpj+fz2nsTqyXof1cL91av9043/a7Hl2Xzhxv6y6ul/WAB1JKi/UznW6UD92qh3Rl3XSpfOjK+a0bZWdKaSrwTH/rIRpgRVH4mE8fwE7Aj4E9gR8AO89hOtsBv8r/7wY8Baw+m2nsDPwy/797TmOtOcjLFOCPwLFdWD+bAT8HdpjDz+8MPAss3c98bAe8DvwGmDSHaawH/DqnM6e/ZyTwfuBp4EtEoGPcHKbVle2U1/HP8n73cD9+W3+39YfyMXRe3nfXmcN0DgQ+CyzaxfXyCPCWLqWz/RyksUulfNgGeBRYYQ7zs3/+/Npz8NnFgYOAC4EZ/Vm/XdpnFgU+AnwHuA946wCn0619pt/HwvySRrf2vW5tpy6lkSr/j+7Pesnl+H+Aw/uRxiLAtsAFwIeBrQcqnS7mZWfgSeCtwI+AXRf0dPK2fh24bk63dU6nWu98YA734UWIetEF+Tif0+20Uy4bluvnb3pLXjd/nNPyobKdfgXs2480yu00rR9p7JD3lyX6uV66VQfesAvlTL/r0d3a1t1YL2U5TtRDLqtfPgf56W/50K16SL+PyUr5cAnwMWCbfqTVr3peTqM8Jr/czzT+CNwOPEg/6zM+Bu4x4Bnw0WTD1Ar3Sfn5DsBjwIazmc4uwGvAPZVlnwP2mo00dgZ+CSxTWXYM8InZKeSJ4M9fgSPy88H575A5WD+D8t/9gf8GdpzNz5e/aan8fKE53E67EAGt04G95zCNjfJ6+XrdCbTjPAHLA2cCVwG75hPPIcDzwGqzmZ+ubCdge+AXwOL5+ZHArcCw2dxv+rutlwaeKD8HvJcIMKw0m+msBfwO2GNOtnOL9fIB4CtdWL9lOkM7Xb9EI/N/Ks9HAXcz+4HzchstkT//C2azUgrsB3wL2Av4GnBpP9Zxv/aZ/NlNgW8DE4GpwE+YswppNZ2N8r44W5W4Lu4z/T4W5pc0urnvtdjeuwzAth4GLAScQwQOX2cOAtU5D38F3jm7n62ksTIwA7gHOAM4mjinzNZx0I10upiXPvUZYGsiELTmgpoOMBn4E/A+4HLgkjnc3vX1zp2JIHzH+19lO91dt512msO8TMjPBwGJXGebjXR2BZ4hyswjK2ktPwfp/JRKUKwse2Yjjal5O02fk+2T09gyr5d3EZ0khwJvZzaDC7SoAzN79bMpuZw5rB+/qd/16G5t6wbrZU7aJ9XOjQuAT1eez+7+261yptH5bbaCY904Julbjs8AjiICW3vMwXruRj2vPCYPBy6i0gabjTTWp2+b6VDgXvrZge5jYB4DngEfDTZKrbfiBCqNVGASsMlspLMTUSEZBJyaT+rvBn7YaSFErYds6fx8RP57NHD+bOSlPHk+BFxXKdDWAabPzomrkubaRK/xzcQoqY4aMQ1OfNUKwWBgeIfp7EL04iwDnJIL2EHAssSJuW2DKK+XvwF7ECPxPpGXbwx8FFi2w7x8kOh9mVy3/DxyBWFebqe8jl8HLsjPRwEfB06bw2Nitrc1MLTy/81E0LD8PfsAYzv87pT3i5OAT9a9NgFYdTZ+R7leLszPVyECzCdXv68f6Xx0NvKyE/ASMBN4W97GRwJfAJYCtpjN37Zu3sbHAqcRFa/xs7GOvwZcUVn2HeB9c7K/VPbZj1T2mdltQL8jr5/h+fnbgbPJZWA/0nkrUQZPm1f7TDeOhfkljSbprk00qk4CPpX36dkNzNZvpz2Bc4GF5+G2XpsIJtwAfJU4B5wzB+tjKlGO/6lu+WqdHtPACKLe8BkqHXLEuelpYP0upPNTYMq8SCO/v/7cX26nW4FNZ2P9tkpndupordLZIv/f7tjeMG/r2yvLbgdOn819plrvvBdYIy9fClgYWHE2t9MGddvp8fI3zUFeVqosP48OO27y+39OBJovJ+owiagHnwes0mE62+X87DE767QujalEXe+9dcsHz0YauwB/B/6Vy4avAlfm9f1ZKuXrbO53HeehLp2yvji9smy2RkTRhXp0t7Z1g/XS0fpskd7ywBVEPeRy4MtEoLmjOkSb8qHjciZ/pv7ctHc+Vpecjbw0OybPKcuLNmmMIOqG9eX4hkSbY9QcrOP6el7HnVlEm/qvwEHlvksMQJmdMrwsf79WSeM9wJX92Xd8DNxjwDPgo26DREX6ybKwygf8g9QNW6VNTxVxIv/fumV3EVHxjk5i9WlQCRgRvSAX5RNYu4rbxsQQ0/fm558jGsFrAocB1zMbldKcxppE4/AsokG+UT6BjGzzubLXb5beIGLI/5XEyK2WPQ9EIOsZYnjy+/NvGAJ8Op8kXiUaZ017Uirr5fD8/GGi52FMPnlcSAdDyoEVgOeA7eq3VT4BHdThOu3Kdsrr5mdEpeAe4JvAtXndvn0OjonZ3tbEqKnNyRUbopf1K0TP+qC83dpWBunb8/d1oscsESPyTiZGOF1NBz1DlfWyXF6v1xMN4MuJkVsjiMsJWwYfG6TzZaKCfBkxSnBz4IhW24qojP4x/78wEYT6Vs7Lnnk/fHE29p21iaDYCcAn82/ZG3hXm8+NAIbl/7cnKo3TKvv1bPWKNsjPJ4A18u/5OW0CLzk/K+b/h+VtO4Nah8DmRI/txDlMZ6HK+p9Jm1EU3dhnaH4sbJCfL0oHgbUmaUymw+OpG2k0SXdNIij2aWCjvOwjRNm3TJvPNt1OxHE+uoPf1e9tTVTuDyHK3+vzvj+ESnAYWIzOzgdlRX1fYhTmg3n/WYwIHF5BBDvabfMtgO+RGzB5fZTbbp+83tteLtIknXLdvJPOAmPdSKOsW5Xn/mGV1+4hj7ihTWO4TTrfpfMgaKt07qUWGFu3RRplYOIUYjTIByuv7UmHo86JcuY58qV5xBUB1+T1vAQxhcBvaTPChDi+7ycfh3WvHUQ0GNsdT/V5OZIYOflO4txyJjCcNufcnM7PidGpo4GbiHJmN6KOdiywSAfrZkciqPc6cT75EXEp2p6drNu67fTevE53Ji4hKzsFOqmL7EFcwrYs0Wk5qu71xYkRgy1HjlFXB64sXyRv93PzY7sOf9MRlWWzO4Ku3/XoDrb15zvZ1vXrhb5tgxGV/1u1mZbN+8spwC3EdCavE4G5M4iyuW0AKafVqny4G9i2gzSanZsWzss2y/lst61bHZOfyNtriQ7ysyWzluPDgSWJ+nXL83WD9NYh6nlnEJ0+G9JBPa9u//1D3Tq/iQ5HrlNrM52a18cpeV+7kGgfDG61v/iYPx8DngEflY0RDdT/Ry1QUl7Ctl1+bQyVyyLaFNBvIxpUZa/fQsRlHZMr7xnfKp1KGmvXLT+ZuKRs1Ta/JxEn639QqbDl164mKhtXkBvwnRYg1BpCZwFbVZZvTAQuGga18jp8Hfhwfj6k/E6iIf0QUbHbmxaX0RAn8R9TG0X3PWIkyUVE8OfDxImrWT6q6+VDednqRLBxU6Li16dHpc36WIF8bXwuiMsGw0rAC8AB82o7VdbN8vn5PkRFeTMa9Ey12+b92NYrEI2dXfLzpYhKRrUS1/EcAMSJ9668z3yKCEw8kPebTYjRV02DqZX1slx+PoIINu4AHEw0Yh8jAlxN53VpkM5wokK5F9H79U6icXYWUeFs2BgHplGpbObtvzxRedqEOMmf1sl2Ii4x/ThwPBHQvZOo1I6vvGeWHtK8r+5JvtSXaLxfCHwgP+/4ktC6dKtBupuJnuNORyrsSj4m8/NDgXvz/5OAfxLH+XPN1m2LdO6h0pgjKu0vtdiHu7XPNDoWns77zJLEMbZ2q+3cJI3646llELPDNGZrXg4i6Hk6cUxumZdtQTQcb8zrp1VgoeH2Jl+ympctRJyDGzZE+rutifPSN4gGyylEr/w0Iqh1TX7PMKIB83qz/SW/b/38nmrn13SiwfdxYlTxtA7X7Qzgs/n/QXWvjSUaMi3z0ygdasGA4URZcwYxGnPzuZUGs9atqiMXTyPmNVqcCAIdS5NRKh2k80LeVmsQUxI0DGS2SedU4rKyEUQHx300GLVINOz+Ahycn69K1PHeWfe+lpeCMWt9pqx3LpH/7kcELD5FBIVa7X8zyCMcifJ758prW1G7BKtZOdMnL5XlFxPH8inE8bg5cXy3Kjsfp3b52UHAF4nRQ5cQ58yFWuUlv/YW4rg9kuiAWo6oh69CHoFE+07qzfJ2en9+vhdxnruKqFO0DSZVfs/yxHnzcSr1KaIcPwr4PfBoi3Qa1YEHEfWp84jO2QOIDvQXaX5OWTen8/bKMVkGzVPdezva1jSuR3+HNnNpttnWFxOjzhYt89IoPw3WyxvTfRABwwOB93Twm8YDtxGj+fYkOnGvoa7+Q5xzR9LkihDalzMvUTlGWuSn0bnp/vz/CsTI0uuIgGuzumK7Y/Lj1AZyNFy/lc+cAZxV2WfKsmZtolN01XbHQiWtsp73YaJT+G3EsVaeG1rlozwm30WUb78ljsdLiLpzyxF99G0zHZOXbUx02J9IBBIXa7d9fMyfjwHPgI+6DRIF6wPUKjtlwTGaqNx+nxYVyPzeskDfiahkv5eIol9Vec9axMmk4YT+lTR2yWnsSTS6zyUqbZOIk/Fa5MZMi8J5lbrnKxMn3i8RFeyt6byyvm4+MZxNZdRGztelxHXmt1DpXan7/HpEwOCoBst/UxbMxCTkdwJjmqRTFr5jiRPLu4meoh2IBkMnvTlvVKxywX4vEVw4m7pepTaF/BjiBLVZ3Tr+FnFSPpI2FdJubicqFT2iAnp+/n8CMerlMiqXd7bYb/q7rXcjApxbE/MqXAPsll+7hrjEqe1QeaIy+lDO9/3EyXOHvP8fl5//NxGEadpDX9lnqiMvVyAaE5cRFeYRed97luaV/jKd+srWMKJ8mJ6fTyMqtxPr3lcdBTek7vk2RA/YbURFod2IpnWIisSx+e8V+XPHEiMYphAjVT5IgxF+xAiAOyvPP5W36RxVJIBxROPpmLzfPkwEkH5GbeRNq2NgjfzeffLzjYnyeNW8nctK3TRiLquGAbcm6XwfWLmyT72baMg2vcyzi/tMeSxMI4In5xCVyufzuvoxbW7uwqzH07XUjqcLiEvu12xzTHflmMzvX5PoqT6TyjmRaHSek/9/N1Hpb9hLX9lO78jPN8v77aj8/FiiI+i3RMNtsRZpzPG2JhpCZS/+nkSA5Tbi/LsIcY65jgguPE6TUSFEY+GQun1nUaIX/Cs5r4sQ9YxtaHFzFqLsPqOaViW9a4gyeFuibGy679SlMyT/XZYYvXZlXt/HEvvv7nMxjfF5/zqwsmwGUZ9ZLz//QN5Ov6H5sdQondPyPrIecSnYDXkdPU7zDrZW6axPHNfnEo23WUYUEeXRb+uWLU/uQMq/rdPOxnJfeaMzgggS7k+cQ8oG+rSc54ajkYkRS5/M/y9ElBFnVX7vOpX3Nisjqvva4PydZ+VtPZToPPwMcZ55psX6LX/TUkQA6rK8fsvfciDtgy6rEZegJeC+vGzJMv/0PXceRoP6EXGcHFV5Ph24O/9/PnWdkR1so3XJlx7m54sTI2Q/SpyrWk4/QF0dOG+nC3Jelqy8bxpR3xlZv62IcuZ/yYEzakGxNYnj8VQ6mIif5vXoHYnLBd9W9/6W+0zdtp6Rt0m5Py/XKp369ZKXrUiUM0sTZXrbaUnoGxA5i8p8f0TZfiYRQLmKuFlMwzKYxuXD6fmz5aCGRdv8pvpz0+Z5vU4lOmNOz8u3INqIDacfoPEx+RmivVKu32Va5SUv/1DlO8t9ZuX8O4/rZFvn18p6Xtn5+Udgg7yNyo7Vph0CxDF5XOX540Q7eU1qVwcMrv5tkk7ZhktEMOzrldeGEh2Yb2n3e3zMX48Bz4CPBhslCsRHqJ10xubC7QpiqOcDwNEdprUDcdL8SC7QtiR6DTYnRin9nDYjk4gA20XEiesa4kSxN1H5O4kI6OzR4HNlgVktVNcgGmKX5bwMz4XZ2zr4LUNzQfjZvI6WyN9/MXAHcbL+KZUTSZN0JhON2o3oWxG4iqiILkIEqzYlGnJbN/tt+f+y53Ff4mSzE9HQ+SQNGs6V9VJN426iB/RMonK7PtHou6CTgjUXyi8QvRXHEY3Ua4mK4ItEI6tZj1BXt1MljUFEwO7deXs9l/Mzleh53r+b27pufZa/aVfiUqJr8m9JRFDgfmojcWb5/XV5WR54OW/PXXLeTiKG6N/VLi9N8leedA8iRuVtWpeX3YmRQEu0Sic/r1aOPksERMvh99OI/XyDRr+tLp0tiQrC9MrzZ4n54ZpVdNbM+9w5RGXjIGrB5XXy82WIsqbh5RDEnGY3ET3VF5Dn2auup8rzMW32mROJY+jbRICtbDxsn79jkcq6b/abJuT99iqi5/mLeflxRABp2fx8F6KzoNl+M4G4DOQLef+7imiELEM0wO8gGl7rESP0Rvdzn9mt0T5D32PhB3kfeTTvs78mGiGr5bw2vFEHsx5PXyJPUpu3/dP57000D/J17Zis7HtnU9dRlPfDarD1SFpc9lTZTlcSx/g3iErypUQwbEeiMXJCB2m029aT829vOd8Nca6/nxiZciVwU+W106nNN9duxO3qRI/6FUQDZNOcz9vzvvKLFttrLeL4r45SXyRv+/+Qj8W8P36bJnOx1adDnEuOJ861e1TetyFN7nbdjTQq2+lh4nx4X96uE4hOheOIkX5L5e97kiZ3U2yQzq+oBdc+Qi2It0HOT7MRaNV0vkucw8tLnN9PNPrKIO3KVEZN52UXAl+pP86Iut599G2ktzqWZjmnEPWZW4n9+fvUGoEbEOfhWe6ATByTPwf2y883Jq4u2Jta51/LOgR9y7wdiSkdTiWOp42JeueZ+fXxRN14vRbpLQf8mSibjsjrZ5+czrvyti8DK83K8kXyulgpb6vN6l7/IBHQ/D0R0Gx0/j+AqBcOIs5z5XatH4nT7lLT5akF6ZYi6mmfI46H58nzIxEdKM3238n5vVOJsu7H1G7sslDO4wpEML1Zx+OkvL+WAbb1iYDyw0Qg6R5ykKLDbV2tR99BBN2HEe2NDRp9psW2voXaCL1P521/H5XRyW3Wy2Si3P4u0dF9FNEB/Z38Ha3yUP1Nl1AbIf12ohw+nlpH9SF5fXVSXn2PKAPHEh1iZxFthve0+U1lfeYa4tx/D9Hp/93Ke4YTo0KbdSDVH5PnACfm52U5fB1tbipB1JWfJoLNo4k64gM5T0eSpwNpk0ZZzzs9/47b83aZTtRVv0m+WQjRKdtw3VbSG0mca8urXBIxUvQmmpQL1Oon1fVyNvCFyvcuQZRPTzCHdyv2MTCPAc+AjyYbJirR43Jh/ARwSuW1d5cH4GymuX0uSC8mKsc7Er3hLa8vz58to/uDiN7+23MaH8iFwMO0v7RyBFFpvJbafB7jc6Ha6YT3yxAnqfcTIw/+Sq0S+h7gq5X3tjp5jSJ6kz6anw8hTja3URneTFTEnqNBxbZSOC5G9AzcTgQiFyYCJ+dROyk2bOjlQngsMYz7aqISsDlxcv8IceL8CS1GalXS3jZvz6OJ3pwjiIrzRsQJ6Rk6mNyyG9spf2ZZoiKwNtHI+FDltaOBGZXnH6YuQDs72zova9YwW5FapetkIjhQBjcWytusnJtoJA1u0U1UjCcR+/vjwCvUKueN8rJ4o7zU55c4tt9RWVaeiBev7Dut5lIbRhyPi+TnE4mG67aV71iPfLy3SGca0aA5rLLsYODaDn7H2kQg4P3kUTJ5+SbEMXBW/e9r8P+Z+XF63l/fXnmt3L83JCrirX7HSOI4uIU8z0TedocDZ+fnbfdhIjD8tvy5sqG6CnFMHJifL9kuLSIwcQDRobEE0WA9mgi+bFJ530qd5K3ZPpMfi+XvadUAXimncQcxGmRRohPmWGKukIUr720WOBxd+f8UIshd5n9PovHb7lKEfh+T5TaobK/y//FEpXb3uvesUt0/69IZSzS+ryUuU/kx8KX82o5EcKrhnDNzsK2XpnUv9JpE0Go80QD5X+Az+bWN8msHt9pG+bVVifPQpcQxWk7+fSC1XvHNiON+apM0JhDl3eE5jWuIgMIHiAbNLkRH2wWt8pPTeZQozzcmznVvo1belfMM3tkmL/1KI7++HFFObkDtEqujiUDxc5X3XUCLO+3ldCYRAYFq58RHiWBYmfaB5EZ6i3QmEKNryw6NbYlRyBsQdZV3EOfvHxLHbLXs/AJ1d6EkjoeHiXPoerQoExrkZ1Ei2HkbOYiV1/kl9J2b6CkaXxo8jhhFeCIReL2SODfcR9S57iUHztrsM2Xn4qepXT55LjHq/DFq86h+ixZ1WKIO/RS1kWL7EMf526mVOS1vsEE04m/I/++Ut0VZ5p2c95Uv06JRTwSvvkrUr+4mLuEaTJRLxxHHWMsAXaWsKkd/foAY6bU9cXyXo/NWIo71c9uUD+sSZXh5N73qyOQDiXNDGTBrNOJ7cl7/GxF1tI8QAZaPEG2Ys+v3rQZpVOvRhxDl5Wb5d95BnJu+QZsgW922Ljv3LiHKjNXzb32cPEK5RRqrEKNpb8r5WDb/xhvJl3M3yn/dskF5G/yYfCk/ETj5MLWRxOXvPoLogGt47qdWPmxABIX2oxbk3ZF8U7U2v2kNYl67DxAdM8cTQb+Lc7ozcl7bzdG8MHE5ZHk+ugD4PyKgtQxxTjioTRrrEWXBzUTH8n8Rx8ZxRHn+E9rc4TSvk82Iuu6YvOw7xDFYnosWz/k7l0pnYf32Ikax/Sj/vwGxTw/L6+jbjbZv/fbP+8h38m97b96edxLnxw3y/rNIu7R8zB+PAc+AjxYbJwrXB+h7q9+RRNDjlAYHeLsDeAZwXuX5f+dH/SV07Ua7fCQXYmsRFaD35UKu6Vwulc9OoBLkISpfH8i/dT2iEr9+fu10GtwJiejJOp1ogGxG9NZuRlTE3tEq73XLRxEVrAPK9xE9frvQd6Lhg4H/ofmlFYsRjYV98u84MW+jt1TeM8tktA3Wy5q5wH+avr3gJ9HhcPv8/nWIytq55DnliMruTKIx3PLS125tp8q6GU0edVNZfh61ytgHid6wLZk1cNhqW+9Ted8Uojet1bxPy+b9dEx+vhDRM/SjvF6WIoIDp9NgolaiMX8mMeKlVV42zq/NMgKoLr0hxIl9lsleK9tsd/Lk603SWJTo0SyHjyei960MIFaDpj9vsQ9vQd+5nt5GjIgrb8QwgRaX4xLH0qjK8y2J3snTicrUDjSYb6d+e1eWP0KufOXnGxKNwg93sP8vRPQa7kNURrYiKrW7EgGLp+j8DraXUptDIhHHVMPbedMk6FJ5fYm8r/cJlFTW31ZEY3bj2dln6NtIXoao2I+lweivyv5wNrXLB1fP33tnfv4B8mTmzDpib3Dl/88T5eVelWXbEg31Ti/h6tcxmT8zPOdl7/x8KFEeV8916xLnqqdofrnIUkQjfj8iUPwrokw+hhi9WgZeVsv719YdbuuN69b9B4jz91k0uOQ+5+P6vE1uzOvkYaJy/Z28Psbl925M83msliYCa9sRFf0H6Xv3rXIE4idpHWweRTQ4bwOuriw/J6/Pz1b2pY1pcmOKvF9OJuoP36ssr86fcwe5h5/Gl632O4269JbM2+MbROBvf2IU2bl5H5plVFSTdMZR24dXJgIvh+Xni1Tet14Haa1KNHz3zNvuCiIw9l0icDBL8ITo1FuO6IzYlgic/FfeVx6rbPdVyXPxtfj+xfNv369ueTkXZLkfn0pMjt6o03AsMUrsE0SQ7YJyPyUCBstTuYSM5nW0ZfO+uznReP4Icc57X/599+Tf2vLu3cTxODxvm6uJc8PISn7+QaXcbZSf/L3lZbzlJeMfJY7jA3Jeqpd0zzJCmpib9C6ijHxHfnyVaNBfRAQN25adRL3qAzm9PfKyW4iyamli5NbZlfc3vTlF3kYfr1u2I1HelGm/gwi0zTLnGFFeLU1ttMwQIijweP6dKxN16HcRI3P2aPGbbiD25eWJ+uojRF1xCNF5slWz31G3rYcRZdEP6duhcSZ1l3032dYLE+eLd+fnWxMdUuUIw02pHEc0r8eMJsrv9YlgarnfLFR5z1jyJZ/ttj1xjF9HnJ/KwPu+VDqd2xxP1+X9dSEiQPvnvH4/T20+6nZ5GJnX8drk6QWI0ZTTieP0fe3ykveZxYjOn88QgcgRlfXxcdof04OIY79sRwzJ+88xRB3g7PzYvNVvIjpFDsrb9jtE2X0v0fY4nw7nPSXqChOI+tn2+XvLEfrldDIt53v0MX88BjwDPtpsoL6jl5YhGgi35kJlHaKXaIvKe1oVADsRvSdbEyf0Z4mAwkJEA/x42lSacjqfIl9rTzSqfkpU3soK0+KN8sKsQY91iArpZKIye0EupEcQJ+s7mxVK9L1Dyw7Av4kTz+i8bFM6myxzAnHC+xBREbiVvifSiUTg4QvEqKemwbH8dxOikrNj5bVPEg2IcpLR95B7OBusozHAFXXLvkItQLFqB9tnLXJPUH6+PNEbdG8u/H9E6wBS17ZTuQ/n378/0cN+ElHpWJxohP6ISo9Xg3XSbFuXlyJMJfblS4gGZLNLRlckRv9tRlRyd8zrpLw85zjiRNq0MdRhXp6hg0udiWP4Hup6makdR3sQjZp926RTjuo4MG+jc4lh6WU6CxGVjQeIy1razRv2jpzG0dQqX+sRJ/mWva35vdOozf00nNq8NJdTGwU0y2Wv+f+y4TGEGGWwBFFGPQd8s+57VmiRhzK/dxCVwQOJyu53iR7Js2hzCUJOZ3x+fxk8/wQRIChHTixD9MZeQlyC0fCSVWJkwGHEJUmbVZYlaneg/Qp1E6e322fq1t2OeZ0/Q+2mGc0mT945v17OR7QotVGzv8jbb+NKPpes+/yXiUDLDGqT3i9HXJp5YE5vCWD1Rsd0N4/J/L5diTJmcuVz5+T/1yRGaBxI7NOt5nVZpC7N/yUuoy6DAuOJkcGvUxkJ2WJbT60sX4XaeeQIomH7Io3nHFs77xNl58wJRC/0GGoBuil5XzytxXop99PVid7vpancSZpooDxM1A02oEnDkziH/Iu+5fROVC4vJYLXt5KD8i3ytAPwqerxnv+/OG+bQfm3XU7zsrwbaQwiRkI9T+USovwbjqZ2Sdr+ROCj2bpZiAh0Hl5ZVpYT1eDV9+nsZgXvyJ89myjDVidGadxAbZTlbsQonerxfwRRzn6BqJNdn/eXFYjz7sXA3+nsztLVSzAbzW/2NiJoMYMoHxsFx4YT9aovUguKlcHYZfJvO6qDvAzLadxK3xGtdxLlRRkonEKU+63qIlsS54QyHyOJUcjlCJiRlfe2CxB8mBgpvRsRUC/raIsSZfoz5bau2w82J4LrK+RtWp2C4XY67FAlyts98v+LEG2DdYny9NzK++7I+13Djjqifvc8EbxanRjx8l3i2FicaBv8iKj3f58GxxNR1v+A2kjWhYigwspEffQv+bMHEOVasxHAZT16dyLwPpQIVhxBBG/6TCvSahsR9Y5LK8+XyttkUyJAPK3Vts770peJEYo/JAcPibrDZ4k6dds5x/JnxgGX5/+H5s8fUZe3xfM2aDidQX7f6kRdvFxPS+Z8HEEcb2u0+U0jifp4uf9/m8qdl2l8rDc7d69P7aZfI3Jar1Oby7XtsUTt0sfyMsahda8PafN7yvrZ+yrLFiLKy/JqmYZ1zQbrZQZ5ygWijfY4UW9Yhb4jgluN5hxJtEXKKzZ2JAaflFcZ7EebSzt9DPxjwDPgo8MNFRXaz+RCelGiUn0vcWnDQ9RGNTTruShPJBsRJ73/R/RGbkgEbr5NnFifon2P4rbEKJbtK8u2JqLupxBBsmYTopYjI4YQlcf/JRquJ1Xe8yGi0dW2t5Z8N5y8XnbOy7bMBfR9dHaZ6LpEw2MGcdIo19W4XDAen59vkQvxpnMBESO1bqgs/zgRkJiYnx+S87ZZk7yMy9tgC+JEWV72OiR/9gY6uN0ztYDGCkQFttp7eDa1RmOz/aXb22l83u+uIiruSxMniXJUxHW0GRXXZFuvT1TqyqHz2xINz2aVwN2JoMFNREVuX6Ih+xHiBPtV8kiQZuumRV42znmpNpDaVa4n5u19FJVADxEU+ymV0TgdrN9787q9llqAeGjO13XEJQmbEBXn9ZukszwR+DwuPz+caABsSy3AOrlFPpYiKrCfIypqOxMNtC8Ql1w9Q/sJj6ujkiYT5dun8v5TzhuyNlHxfnuLdFYkju3yMsMf5nyNIQKFH6XvZYHNKk3j8u/+MtETX/asbpr3o7OJ0Xg/p0Wlh6iArpf/X4wYUXQDEXjfkCh/ruxgW5f7zJHUbsW+PVHunlD5jq2IStr2dZ+vBl7vpBaoO5oo704ijs2biUbTCnnfWaJSptxFNMbKy1vPI84JRxCVwx8Q56jv035S/34dk5XfsxtRmb02/46tiYbEXTmdcr65Q/L3NZ3kn1oA4jLg0Mq+eA9Rxn+V2qW6jfI0mlkvDb+KKOuqwfXt8m9evtn+l993AX0ny94o7zsnVfI2S8CDvuf9r9W9Vh6fVxKBiqlEJ1Gzzp9xOa8fzO/9HbkDIKd/PZXL41v8ltWJMrqcEHooEVh+kmjslyMG7iDKqkaX67VKYyQReDiDCDQ8Q/N51NYlAsHbV8qv66iNhv5M3peOp/WdqscR5XWZnxnkRmN+fg/RoN2Q6OxodofTofn13+Y0lqcW1FqcaAgfS4xg/2T9PlZJ56Tqb87b6x/A0+22T6P9p27ZHnk9lyM0tySO1UZ3zlyB2gj1EURA81DiMsZyCo6285ZW1m95rn07UfddijjW7iHKmptz3vZoks5qeX/Ygbgy4L+p3Sxgw7y/7NHq9+flxxD7+zZ5mxxDlGOfIupFfySmftip8pn6DsdD6Dt34JT8O8rzyzuJuyBP6GD9vJWoe7wCfL6y/E6ivtnyMjninPKtvE4fIDoFFiaC33+idhnqtkTnR6N5AMcRVzscRa38HJXXx5fza1vkbXAoDaaboFZevYe+gaTvEefXMiC0RKvtk18bS5wPPpC3zS+J43goUa7fRJubZBHtgTvp2wFwWd7ey9Hizt916axNnLPLc+26eX2UN55ZgQhI/oAYqdxsVPMK+T1vJ4J7XyfKiUWJUZC3UCmj2qyf3YmOmUWonRsHMeuVCw0v+8/b9kfURgsvRO0upUsSdYKj25Qly+Ztu3dl2VDi2PwaEdw6utlvyO8fT9TBVsnr4bNEx+4GlXyu3Sgfdf9fQpxzViDqyS9SGzF4IHHeK/fPVlMh7El0yFxPnFs+lJe/jSib7qBWn/HSyvnwMeAZ8NHhhooC5CaiErADEZBYNb+2ZH6+RX4+hrpLderS2oRaRPujwMeoXZqxM1G5aDdh4VuJk84Uorfos7nA/mouoJ6mQfAn5/8XREP5XuJEWb2byWdzYXsV0WvadtJCogfuPmL0xeZEpfEUoqdqJm2uV2+S5niiQnwclSHxOc39aD6Z7jp5W7yNqGA/Ru0StPcQlaX18vP1q7+9ksauOd/X5nWwEDHS69u5UL2LNkPKiYblQkRl8sLK8k2JIfvljR2azbkzN7bTkpX/304EOrYkTswrEJW3UbQ+mZfbejC10Vn3VF7fKK/3VnNZrEMEC8uK/fFEw/PEvH5+SYtLD1rkpWFQjDjRNx0WTlQoryEa9ksym0GxSjpL5LyU842VlxN8mb4T2k+gRXCVqFAuTFQof09UuB4nKrifI98RqcXn1yAaQVsRgeQDqc2rMR34SGUfnUiTkV95WzxMLTA9iqhEnZG32SfbHQf5cyPztilHBZTb5cCcxkH126xBGssRl9wsSvREX0A0GPYmyoRjyDeToM18PkTZ8DsisPF3cllCvuyo8r6laD4pb7nP7JHfN4MI2JQTZJc9wPsRFdRhdZ8v10EZWDuGKFs2pzZ3z5pEoG0kcXxWL/+oBi83JxpMU/P7/kDM0/UeajeOaVbR78oxWUlnPSJItEJ+fJsI+J1PlJtDiMbrzbS5+yWxH/+UOJ+tQ5wPDiM6gB7O631Y3geazYtV5uuwvB7KEWDlqK8RRLBsiWb7H1Em/ILaKOONiUr3yZXj5J68L81yPim3MxEkLffRkcQ5/HrgY5X37Uw0CJoFxyYQx/T1wKl52RSizK2WMU3vzptfn0QE4b6Z97sHic6SNYgA75WVtH9M45vYNEpjGSJo9iniHLUo0Qh9mDwqocmx9GPivHgDMUJlOFFu30gtaLYJ0bBpOIqH2hxo386fK4/rbwDfrrzvdPKlNU3SGZvTKkf7HUgc50cS+/HXiPrJinmf2qmynyWinLyPWkBqc+IyuH2Jyx/PrL6/1XZqkLc+5yZq5Uwn8zaWcwKdR5z7jyQ6Xt7V6Jhp8PnxxHnoG3n9fpCo695E3zuur0fUC5p1Po4hRv28Tu0OeRsSl/n9iNiX200avwIRFBuU1+s/icDptkRZ/By1+niz3/NO4NbKMXwU8LnKa7+g/ejuRJRpF+bfU73T3gNEoO895FHnRFnScJ5Zap2p5X63R17fX8rba9W8vNWIprWIkd5D8/8nEoGkMcQxdR3RkdJ0zr3yO4hjuzoX3dj8W84ljyLLy1eleYB4XP7OTxMjP4cRQeabiTLiDiqB2UbbitqVHuXxvCW1+UpXYdZAUrPtvUfev3bOjweINsBieZ1dSZR3+xDl0epN0plElPd3EXWh1fI+eBdxbNxG3aWVDdIYTJzzP0Gcg75FpWOFKDc/QZShn6N5e2dizutxlfWxPrUR4N+lzai6/Ht+QC3ovW/eR+7Kz+8lj8ZskcbQvH9dSHTMbpK3/deJ6TDuooPpaIg673VEHebovGxv4jgoR+OWI1SbTjlE7Pu/o9aZtU/evuWlnU3LJx8D/xjwDPiYjY1Vq1QfWymIykbMGrlwWIOo2L7cqDCjb4R8ZC6oy8ubhhGV0h8Tlf9BtD74yx7zNYnGdznkejOiEd3wkiCigbkkdfN/5EL+RmqXw2xDVFYWbpSPyufWIC4BmkD01B1aee106uZP6GA9T8oF6XH0DeYsmwvJX9Nigmni5PDtXLhWg2J/IwI35Qieq6hrMFbW7UqVAnh/ao3gRYgK8WdoMpdLXXrV26NvTFQGysls9yMK+zWr3z0Xt1O1kfgCUTktK2PLEw2bhWlyF6TKti6DEc9Qm9z6KqIH9nwioDiMfAOLJumsQFQoT6Ju3ou8bXZvtE4a5GU/osL2DH2Hxpe/dUTeN5+nRRCH2nH8NiKYOltBsQbplaNK6oNiHc9xQFRAy7uLrU80Cr9MjFwYTm1OiEZlxF5Eg+VeanPvDCcajkfn5+vl95ze5PtnkC8Tq6zP7Yjy7fN0cElQ/sxqVMoAojGxLjFaYOe8vo+svN60kUdUqp8jOhP+Qu0uit+l83nLVqV2KeSeRCfCp3M+yrmRRhKBoddoEhDK6zPlffl5+l62V66vlZt9vvLeVfJ2mkbtHDOUOJ6uIMquWRrSNJgThSgzTyaOrUeIIP+n6Gxew34fk3XH5veJEWzlKIMy6PEEtXPXFjS/E9daRON9JWLUwEnEvj+DWkPz7cQ+3fJ4Jc6HJ+T/R1SW709UustgdqOOktHEZUVDiAb8V6gFxbYkGp93532z1b47Me9r5xOjFy4n33ym/G7iePgJ0ShvNjpqGLWAyAZ5vTa8fJJoHDW8EQkRbB6f99FEHKefJII6T1CbR28jmgc5GqVxGXEsLZnfM4YIIK3aYt2sTQ7S0HdE3zlEI7RMa1+ajLbNr69IBIjL8vwbRMCinPNxvbz+O+qsozZK/ZfU7qJYvcHJBkSAfse6z03P23NLoqOw2mGzNH2P45Z3Qay8r2ywV+cU7DiwlvNUvTnSU8QotsnlumuXLlF2voXahOZfI98kIz8vj6NTqdxNtcn2Lq+y2IA4psqb6SxMBC3WrLy/1ejx9Ynjppw24Fc0GBXd6DcR59Nv533s03n/2SmnUX/jplajVMaRy4T8/D76BhbeSu1KkwtoPHdvdZ/YlyiX3pafH0UEw1vW8SqfH0XtcrYNK8seIero7+ogjQnEcXxwfj6DGOl7EHFcfJ04R91Hi8u3qZVVK+b8nEGtczoRx+PWbbbTSOKcVK6PZyv74BiizbNZszQq23CHvK/dSOzHiagPPwA8WXn/tbQYKZX3tTLNtxOjncqrJkYQ56SWV5YQ5/1yH9uVODcsQZSlzxJ16sPyvvAszc8HY6ld4fIt4jz0Q6Jzawr5uMrvXY4G5XDeNmvl736RaEveTLR130LlPNXi9yxH1DWm5HVQBrg+ll9/kCaj1+l7qfPnqXXevp0IrB2bn59FHsnYQX5WzX/3I85J76+8djW5/PEx/z0GPAM+ZmNj5UAVEcGvngTLeXnWyIXb+3Ph8BOazzGzOLX5RcrG2BG5gN298r4l899WDZrtgVvy/6vm732Gym2jW/2m/PfTRGV8OrUG91bUTu7t7nI2hKhMHJ2fDyZOVpfTYKh+o9+T1+1CRGXrVPreyWRJYu6P64nA2Ifa5OeN4bvEvAF/ozZnz4eJkWPVXs4RjfKX3/tV4sRVNvBGEA3Wy6ldotnukr1NiUDHWfnz+xC9NDcSPYK7tUqnW9upso+cQm0ugoVzXj6dn59EZRLbBp8fTFSwyjuKjiR6Z04iGvFL5OVrEZW7ZifD1YiT/1cqy/YkKhXlnGFlWs3WSyL3zDY4HhchgnjnEiOv2g3bX5Q4ebetNHawjjckeg5n1G/D2UhjN6LxUs7v8RXi8rTliUrpV2kQrCAqE7cSl0vcQFTaRxCVvXOIY3UCMW/MZ2YjP+OJIMBZ1F3OOQe/bRhxnK9DVGy/ThzjaxCXcTUbqTWWvj3qLxKjOz/Xj231LqI8KIOQo4jRK7cSHQ0NJ52vfH4klbuHMuuE+Z0ck0tRC4oNISa3vpC+lzgs2epYyK99lNrlfWsQDfpfUysrhrZKgy4ck/m1Kfl7d64sOyk/hlV+82a0nlNwMFFBP504NwwnGpq7EcHf+yvHwDbUlVvUyvFTqGu8EcfSF6lNjL4LEURpdEnkMOKYvgM4MS/bjOiBvow4TquXzjUcDUeUvZsQI1uWrCxfmrjb9Z053UlEEKTV6JD18+//UN3yZYhRTjcTDf62QeOcpzOpXYJ9GBHEXKrV5xqkcQZR3t5PjHjYkiifq+Vgu3PlW6hdij6JOEdWL08aTIxKbJk3+t7Q4jaijLmFaGiNJ8qdVWgwMoRaXeQh4D9EY3NYfd6J8vTnRJ1r5wav30SeS5E4z25HdKp9lijP294FMb9enpva3k2yRRpvjAolLr38VbnPEsfTC+Q5fjrcTmvR9wYM1fmIvkFu3LZJY3Le56udR+8jJvY/lDaXgdeltVf+TdtQu4vm/vStT5flQbUhvgm1EdU75/VQjuhfhjjfldupbRCTPDdS/n8qcHH+/715fzl4drd1Xr5mu++uvHcZ4ljeND8vL8M+Lqd/LhEMabeNV8r7/XJEPXUjasHPI4jOuuOpBNGbHEtD83d+jkqHMtF2uYtKx0uLvJSjoy4nyuHViI7Z24iy6y4qI/ZapLNY3j+G5n3sCiIY+sm8jk4izv1bt0knER2pX6Fv0OUwojxYcjaPz1WIc//TVG5ck1/bKu9XDUd+l/sH0YFf1n9vIcrAci7I5YkR6HfQ4IqO/J6VyTfqyuvozvxbyqsGWgbPiXpmIjo57sjL7iDOC0fX7x/0rbfWl50H5m0zvbLsBKLeO7hVPiqvrUDUZz5YWfYeouNnnbr3zlYd1sfcewx4BnzMwUaL3q7niJNuecIsR9BUr4UvL/Vase7z5TX2OxKVlbuIHuf7iCHcCxEnkXPya316rhrkZzxRMS9Hnq2QC+fFcwH1c2p3OWuWxhSiAlBetrBm/o2HEhXlj9PmxEyt4TaYCASdT64QE1H7/anctbJFXpamb1BsCaLR8A1iOOz51Co4qfreBmntTzR2yqDYIcRJ733EiWOTnN+jaDB3E3Ey3o3aDQ1WotZo/izR6Gh5Qq/sM58nKsdvJxp3OxLBm2l5XVfnuGp4KUw3tlP+3KpEL9TniRPxZ4gK05FEI6AckdFw2DJ9L29dleipqt5qvGyATyMCF80uDZqU1+EGeRt8gQhkbkoER9+YbLbF/jKJCBZWRyouktfz6USD6F7a3LUwf67lXdRmo4xYib7zwc1u4KjcvzcmguU/oNZb+kHiZD+OBoHH/N37EIH8XYnRGj+idknF+kQl95Ptvr/yfALR0D2TvncPG0rfRsa0Nr9rGrXev0PJd7kkjsGliADGuR2kU44I2Yu+nQsdr+fKvr060fheOq+7S/I6/lT+zeV3Ndv/liUanAd2+t0t8jSEaHCcR+1ccA5154I26/cn1M4FCxPngf2JY/xqmtx4pO546tcxmV/bhWjIrE9M+nw7tbk99qRvwG4lmt/1chlqDa+35333LUTQpzwmtiIu8/0HjefEWptoNL8nb+f35HROy+t8O+IOYa/TZPRZzmM5UnxTopw8Lf+2Qyrvew9xfmg7901+/1J5+9xB30b8qrQI/hDH8v/Sd/LwkXm/vYAYnTKS2giYZpfjDCdGGV5GHl2al28GrNThbyjTuILaufJy4rg6LG+vQ4hGzedp3lmyeF531fV5PbU5ObfOz0+nxYjtvO6+XnlejhyeQjS4jiLOGV8kytCGNzXJ67g6Qqt6980vAPfn/zcizjH1ja2zyUFmIhh6FTHCbPWchxvpMMBF3wn552huHGp33Hyd2txEbyECUV/O26/TCc3XAm7L/1dH+e1PBCvKCb3H0/yytJWBUyrPpxN1iQ8To/t+TuXy9hZ5eRvR+bl9/o2fJUbx7kuUV7PcZKB+Heb985fU6lbDiSDKaUT9vO25pcF+d3pep4cTHR37z8G27mhEYaNjMv9dljgnVEdtt+2sqUtrCjFysqyXjCbKlMsqy1agdZ1iWSrTQORt9iXivLQ7UU+sBkEaBV5WJ+ru6xPnl/Pz9j0tv343LeZgraRTnmfvBLapLL+PqH+8lRZzwlXevwJ9y6qDiPpmedOH/ai7S2Sj/bCyDj9M37kRh1Crp3yEOAc1m3NsNDEH6355PV1EnCOXIIKbHyY6I9cm6ltLNPk9j1HrLBqeP1dOW3AGDa7MaJDOvtTafiOIc/+zRMfmJjQJwNf9/14qwXXi+Pw2tQEfTa9sqcvLKnXb52t531mIqB8fSu0GRsNo0xHqY+4/BjwDPuZww8WlQGWjrlFQbBhRAf8m+XKfus+PJwIS5YSN51Cr5F9DVFyOIk60P6Xvbagb3blkNyJAUq1YH0DMe1JOOtxuvoQtiREYnyTmqCkvBzgl5/Ur5JNILmyXrPt8WbhvRASwZuTnXyAar8cSJ7/DW+WjLs0liREddxGNvp3II3qI3r1PEBWihiOciJPA5pX1cSd9J6jcJG+/ZYlK+Sw9/dSGgq9MNFjfmHQ3p3kZtV6aVg3FQcSotx8RQ7rLCVLHEpXVJfPzhYmROc3mfurXdqqksxZRIdkjPz+SaLRU53xoeClkXTrHEyNKRjTZN7fK279ZY+htRNDznrxeRub1egNRaf857e+Ktx5RuS9/y15Ew/VMomJaro9FiWBT07tNdftBP3uiiF716l1WNyIqXwsTx/2H67+Hvj1qK1JrpEwggiPlsbkZcUyVgeNmFZ23ExX7TYhj6uQG7/koUW41ncCe6Aj4KXEsb0c0XobmY+A0opJcnWujXY/2GUT52VElqe6zZXn1PSKgPIwIKJxGVJYeoTYidAmiYtps0u5yQv7jyJd4zOG2Hp3Xc3kXpUbngpZzYxAj6X5IrUPlPKKxeQBRlr5A3xu3NLvrXb+OyZzOnkRl9kFql+TslD//zrzephMBk6aXyFXSOytvl/uplePTiMrue4gG01M0KGvyvn8fEQB4jNoIwR2IgNr15KBtmzxsRARUykszj8777a7EufYZoiw6vtG6rUtrCaIhcweV4AwdlhlEEPM5ahMMvzPvP9vVvW/fvG8PpXGDc3lqIwLntCH+RhrV30CMmnmEGIlxPHHOeZbmwagJREB1f6Kh9ihxPCbiGPwbcN1s5Gt4Xdqfyeticl62aT4mmo6Szu+rBn+uzPvS0pVlq9HgjuBEw3LpvF1OyevgdqIsvZLK3Gut1n2j7TaH22kMteDPtsToiXJU21jiXD223b5IrRFdDRweSDTIpxMjLC8jghUzaTMPKjHn0reI47Hcd86gs8DYGtTumPgeoh7zA6IxvRhR71u11TokRjPtSdRjPkDUya7Pr51KBLc6PjaIIMaDRKD641TuKNvBZ/u9ramd3z5CHjnZaB+jNnL3fTSZG5kYRf04EXjcnAhGfary+kPMRp2KKKeuzttnhbzsCKKNsFz9b2iSxkeIc9uiRPvgirzN1qh7X7M6zbbEdA7j8/NViUBd9c6SLUdY16X3fiIgXJ6XdiNusrZXJ/mp7GfHV7+78tqyed+8hkowr+49axOjqn5N1GuWIdodZZkzlRhR+eH8+2epNxFt02foG6RclzjXLJbTbXkpOtG2qF6GOyTvQ2OI4/LrtLkJVF16BxMdJrsSx/r7iHbOlZX3TG6TxiHURptPzfvaT4ng9fNEfXQXoo7R8UhpH91/DHgGfPRj40VFbUXiDkbvrywrgySjiUprw4YStUraKbnQGUpUkh8gKh0PECfqA6k17FcmKhCz3IqXuM68vBPI4flgLz+3K1GRW4LW8zVsmd+7SS6ETyMqDIcSIwhuy79rFNGzMkvPE1FB/gTR23Aq8KPKa1OJRkDbqHxel/vk9bBNXteP5fV1MDES5utUJo5skVZ5B5dyfQwihi5/OucnESeE0+q3F7XLKi4hApjrV167glrlp+Vt4PP7NyYahmVQbFmiklFOJjoub+O1aV1J7vd2qktvHaJSUL1060iiB3Xlyj5W38s6PH9vOcJsaH6sQpyA3pHz9jMimLdS3efLdBfO67mcaHUT8h2jiFEC+9D+cpO1iYDiAXlbHUJU9I/M62RtouL0IjE/1SzzfMyvD6KH7zJqt7M+h7rKEbVRNc0qgpOJy6tOqaRxX15fP6DJncQqn1+9cjw8Qt/LyT9E9FauTjT2LmuRziQicPOj/LlFiPlLvkJc5rMIbS5DyHlYkkoPcbPf3cG6LSeKv5Ioj8cTAaRt8zp7JxEQep1o0A9rtC/mY+gsooyb5Q5xs5GfUfk73kmLc0Gz9ZL/rpvX43iifKs2vrckys/h+X2H03ckTFeOyUo6y1C7/GYbIghRjlY5mAhKXdVmnSSiAfQT4nxb3rxmGhEYrU56vSPREdToLn2LkScKzs93IzqPphGBmLJzagRRVk9rkMbyRBB4UaJB+c+8ffYgGnvnU2nwNds36Xt+e2uj91ILELwxcqBBOhsQx9Iy+XcfX3mtvGz1fVTultdum/XnQd8REOsRgZb30TdAtRVxqWazS3omEMf294iGy+pEwON8osy4izxPUSf5ruTnRKLDpCzLyvPwp+h8pNRlxDm7LG8HN9m+9XdB/CK1+USPJUbcnJefj6TJOXZuPYhj+NfUgmKDiUDUs8R5+x0dpLEecTnd54mg1jeJOsiSxCj/r+f3TSHqAI1Gc5Zl+a3ULmEckvN3Cw0Cls3WEXFs305tIvHvEMH9s2cjjU3z73gLEey4nxhB+y5qx+N0KiOnm6SzIXHOuJoujCbux3Zegwgub93k9QPz/nw2Le7ySAQ7vkgEey6tLL8TuHk21u+i+Rg6gtocoWOIEZy3EvWApjdfyPvLIOJYLkfwDiPqdhdSm8alOkigz3zNlWOtHNV8Sv79t+X3HkrtZiA7tvk9iWhXfYVaPfitxHmlnCJlOC2uWMhpJCKA+pG618oyamTel16ndrljo3JnNHEsluftMii2et6GfyUCRE3rv0S9uXrzkoeI8vgDxHF5NZWrBZrkYyLRNirXyWZEffNzRMfb9+k7t2yzkWNbEPXFPfNvOI6ox+6at9mVxHn8Wlq0A4kyqAxallfGlHNQv5UYwHEe0V6Zo84hH915DHgGfPRzA0YB8uvKAVcGxVYiGpv114rXBxbqe2/2pDaaY22iUvm3XKisQPQGXELrxtG+wKvUovXDiEr/BQ3e26yyvQq1ieKnVZaPJ4J3q9L6EsbyxHMRlTvWEA2jG/L/KxKXAk6l+QTMo6ldtvM+4F/E6Imjc/5OIkYHtasYlxNaTsvPy4mGryVOOMvldXs1lQp3XRprkwOP+flpRIVjKrURgJ2MsCrXTTmh6IWVPH2ZFkGFbm+nyvunA9+oPD+MCH6Ud3ZatD7/lecrEBXIM4kT1qNEw+WHRKDhY3m/Hk2LgAFRYfoMtcutbqAyYT5xIm464XxevgTRkCp73c4lGtJnEMHaHxIn0gNp0iibXx9EReBJIrD0E6LXb3Gi4nMsEdhsGpzNnzs3/38K0eh4gCgvliEaA01v4V637y5ErcJzPFFZq/Yg30KL0TdEL/VSRJDhFKJS8nuiYnIndSNoWqSzGG3ubtjhul2eCOKvT1Ss78n7yl1E5fknRKW9nIumYaWSLlaoaHwueCXncWFqN85o1QO9MXl0TT5+hhAV/0WJUVqv0+COg5XP9/uYrLz3rcRNacqg2OZEQOdCosK8XgfrZCy1yx62JkaKHVv3nqnECJUl2qT1buLyyWl5nT5INGAOJMqL3+X8ztKgqRwHaxONhVE5nWuIS0BWq3v/6CZ5GE3f0aCNjrXtiHL1apqPul2CCIR+vbKPVgNR11MLiE8hNwjm9oMI/J1O3U0JiPPTI1RGXDX47CLkuwlTG5l9UH4+iAiUbVR5f7v9bwyVu6vStxPzbKIxvDKtL9G8gGhIL5mfL13JT3WU7iHUBSKI4NHNlee75vWwHFFfu7Cyzed6w4yob7y78vy0/PumE/Wy39HBTWhyOrsSQe4lqDXkP0DUicog9J5EMKThDWiozR1ZBsVuI+qtCxNl8wfpe9lao8b4EGLUZjnidhhRXz6GKNffSpt52oi6zA8rz79PrhsRnQpPAc+02+fyOryvk3U4D7b1qjQ45onz+P3kAEVe7y/SvA68CpVJ6YlgyfWV5++mculgs/WT95OR+f8xRODx8/n5ksT5tuXIa6Ij6klqd8ccRBy/GxHnyTemn8ivN7wzOREAPZsIuC+X9+Pf5f117/wdLUeT5nQWJc6HE4mO1zLIO4wYafcFagMXNqPBqCSio+pp4IDq+sv5+gZxLr6JNp2B1ObDnkGcI9fM2+40oo71S2ojyo+nyZ3OiUDUA5Xn9+Xj4ehyW7U5lkbmdbJp3lfeT62z5kiizbBIqzTy8vIKnf2J+uG4/HwRIuh3KREwa3lzq7xOFifK8I0raXyWqF99mNoVQs45NkCPAc+Ajy5sxNpcP+8mKrprExXzs6gNE16p8v6Gvcf57+bE0PZyjpiVqd095aR8ALearPgIIjBxM7XLgNYmouMfqXzPJKLgf4zKNdiVdNas/65cOB9BnEir1+W/0UNct2x4LsQOzcvGEpf+vT1//6NEY/MWojex3fw56xCBtUScxD5JBH+a9lrUfX5tIqg2MRfI1xGXOCxHnBivofM5Vc4iTrxjKtvpx3TQuMvvH5oL4ivz83KkzYX0Hb2xOBE4WLVJOv3aTpXX1iIaKjsTJ9+niUrSYnmd3UmLW3zn3/9SXocr5mUN79BGk/kN8mtvJSpFuxGNuYNy2gcTDcMv0ebueNQaK4sSlYCbiIrhifl52TBfJq/f+X7kGLXjdjeiwXBu3meuyOvr8rwvPEvt0uFWlfZLiBFAqxC9gZ8lgkANt0vdZ6uTLH+IuPPQNkTvfHVOiElt0hlMjLy5IO+vPyQq6iOJETvzNHBJrcK2Ws7TMURDq5yUd6X8+geIgFJHd8HsR37qzwUrEpcZrUT0CH+jciw06+CYRJTx61XfR4xm+Dtxyd96bfLR72Myv7YNtcvgNybK/2Py87WJAFBHxyLRi/wIfRvK5W/blSiHmk2AX44++w21EZhXEA3qDxGV/yOJoHHZ6FqiPo2655sQIwbeGAWRlx9BjMx8nfZ3KWtUL1gp53P/vB0ep8klaUQD7FvUXeZClA0/ycfWBOLY/yuzMbF5P/bhS6kFd6tBv92Ai/L/w2l9k4HFiTpCtZG7JRGkvZEGN/dpks7SwNX5/8HUAjhTiHm/tiDKwyebrRsqHU1EMP1m6m7WkLf5iznNRep+91X5t4zJz1cmGuHfJM6ztzIADTKiY+2ifAyWge6z6ve1Rvto3evLUjv/L0eUD0cRDfWFaHDX1wZpbEF0TLybCKA8TtStxhHnmA+3+fxEosyrTrhdXhK3GtFAb3nJNDEy8QqiHvR1anNYXpm36x2V97bq7Jsv5yuq5pmo732b2gjIbYjgUstL24ny9wpq5e7IvH7voYNJ8PNnVsnb6nOVZe8jRjS1nR6BCCzfR+0mA5vk/eUs4tz3UN4nDybuxtluOpmJRB1q9cqyT9FhGZPfvxO1ev0Ioh59CVGPn0qUiacTI/kajTyfQHQYzcjrZ7m8D55IlJs/pjZR/zY0v2v2MsT5ehLRcXM20eGzCnGu2IwIin2ZqPfPElTK3/0cMRp6LWrzlK1FdIhfR6VjrcnvGUYEr46hVscal/eTb+Xvb3ijDmY9z15EbRDKYkQH/qPUXeZM66ttliY6wcpjeq+8Hx9Uec/IRt/vY948BjwDPrq0IaNA/WYuwJ4kRslMICoFnyQqRE2HB9eltXsu3MsG/ApEUOwcWlyXTVQiHiROamXPw1dy4fUZakN+P0QETT5H9Gw0uxV7df6MZalNVrkWUeCvSpsRUsSJ63Git+THuSAbS/TUH0atErYlcb13y+vOiYrmYUQAaZ/K8ur8Sq0aZyOI0TXfyAXkisQJ63vEMPLFK+89igYnHSIA8Ri1yzHGEY2Pskd+OLVbSbfKy/hKeuVNBcoe7O2JxtkdRKPqohbp9Hs75c+uSgRd/kntktMt8/bbhlknR62fA6G881HZ2zWIupsIEJWnc/K63qrutTL4s2vePp8mLrn5IHH51fnEKKNHaTHKpbpPUKvs7Uo04N+Zt/nhRGDyVvJlUHOzfOjGo7J+yt+0Yd5vNicqqRvnbb9/5VhZsm7/GEwEBW6kNh9bOa/M0c3WY5PlHyOCYuUd5FYnKnTlZSwjidE3TedWIRqXmxC9xmWv/LD63zwQ6zn/f0be/8tROEfm3/wBotLd9vLpfuZldyI49sZli8S54Dyio+CJyvoeQoNgNHHZwoPkQCVxiegrxEi4NYlKe3nH3Vk6OPLffh2Tdet0ybxOqyMLls1p/ZBaILBV2bk6McJwz7rlaxAN3xkdrNvy8s6liSDLZ4jyYQtyD3R+fRuiPG0YCCWCCbcSI22G5v1+W2IUxCXE+f9RKhPbz8b2X5eYj6085qcRjYlZOrPy6xOIS5I+QvSg35D3kZF5nZ1DNG72JcqMub3/nk7dJYo5H3eRL+El6iE/ofklXCsQo/LLEQ47EqOvyiDtN6mU3832m7wOHqZy12EiKPZ9Knf2JC4P/HWrdVM5LnYhzmXl/GwnEI2366gEKOlbPykvzRpFjGb+eOW1B6ncRGZePPKx93micVwGC99BnO/XIoLW1RuuNFu/g4ly4djKso9TmbS9w/yMIteVifr0p4nG87FE3eoq2o8MmUCUkWvl9K7M+8vR+fV7aFAnqttOJ+S8r5afXwE8lf+fUdmOZ1B3B8n5+UEEKLegNuJ4PPmGJvn5wkQwpdVNk1bJ63QQEWC5hjg/lPOdfoPObna0Kn1Hmh9CBFOrd/gdRgSEbqFB+4DavH5bEOfkD9S9fmA+Ro8ljv+mHVpEeVB/lc8D9Z9ptT8T5dsvicDuyXk9lfNRHkPcMOXSZp/P71uOOC8tSYzs/ixRfh1fOTamEfXtppeAUwtEHZH32erVO0cR54f3Ntr/646lK4g2z61EfW154tz2SN7272+1XvLvKMuWccT59SJ4446lj9LijpuVdD6Z18dIoqy5qMx/3o+3oFY2twqO7UWcg75J1Bfel5dfQVxF9CAd3jzHR/cfA54BH13cmNGzuSh5dA/Ra3Ef0cs1OB9se7dJozyo1ycarMsSJ+jPNjopNPh8tSdoCyLAsgVxcjk2H/TXEBXuM4nAS8M7IFbSGUnttsirE5X+e4iKxs/JgbVcUI5q8PnR+fvKhtn9NJiAn+iVuJ7Gd0spr8E/jDjR7EUEOMpKS585BPKyhhMoEr20S+e/J+fCdUXiEpgHiIDCfsRJtuHoPGoBvQnEJR6nkieszQX0PVTmjWmxbkcQ8zaUc42MzXl6hAhgnpXXSduRbP3dTvm1VYiAy2J5nS5LnAyXyK8dkZeNzt+1aN3nxxGVj0Prlm9HNNQeJBoiP6FBQ5Ha/l9efjGJaETvnj+3T14vHd3dKafxXmK/3y/n/dic5lSiAjKVaEw2nFh9fntU1tFOwI35/zF5nf45/551iJ7OW4gGev28eTsTl4SUc2AsTINygOh1vIbGjYgbgXsbvL/s5fyvfGzcSfuK4BjgqwO9buvydAUR6CuDYu8hOhLKYf37ERWrPqNC5sJ2Xj+v0/JSo82IUZALEaOIDsrLt8jre90Gaayet/FU4nL0AyrvWYyocG9W/UyDNLpyTOa8PE2t7F6V6Jx4iihTvk9n57pxRNDnfUQAf1/iXPuF+ry3Wr/5//KSvU2JsuLk/HwrakGYn9CgTCdGde1LBKi3Jsq4W8l3rSZGP5zQ7vfUrevq5SVXESO+y+DllrS4fDivl3LC7w8RAdN1ifPcf1MLPG6b96Ol58b+m79jQt6uBxFzoe2bj5lP59dPI85xH8rbstlouHWIUR/7EnWh46n1+I/J31Od+L9ZA20iMffqpXmdvpz3uSUr79mPGMH+c1o0jirbap3898PEqIx3EY2trSvvXZK+QZelgNeAT1SWbZi39ZS675nrI8iIsqEcVfUOonN3S6JsOI8IULUdIZv3s5+SLx8kgvI3Mod3e87rYxmi/nwXUbf4dnnctdreld9T1nNH533vKzmt0XXvn2WON2qdPpfnfaLacbo4LTos59cHcb66hb4j6r5dOSbL9bANMVqoZfCc6ND4HHHOv40YHfQAdTcda3ZMVl4/Km/v8qYoo4ny93Gizv8ylaB23WeHE22t4+uWH0jfO41uQQTJNmySzoZ5O29LdLJ8L3/3YKLuezS1TuNW55Y1iIDu49QCWeWdSQ/K39HuZh+DiHPRxdRGqX6NKF+mEXX7w/LyCTTomII35ke+jTyNRl5+PHFMX5df+1CbvIzM22ZK/v9y4pz9FiJI9isq52ya34V2nfydb1wZQ9Qjfk6T+YcbpHFGXg8zySPGiZG738q/6Q46m/t0baJuW44K/wa1cmJb4rzV8qoHH3PnMeAZ8DGXNmw0ZG4jKhffIhrnR1C7dKRtRTQXxifnQqTtHcDKdPNjUOX5SURF7XHicrf182u3UJt8sGkPXP4tdxGNqg2Iis8ORGVqw1xw35ILqpaXPxG9d98mD6mnEgQjGlx7tPn88tQuf1mPGB69SeX1YTlfnyCCS1u3WLfHEyee9SrLDyaGyt9PbYLnrerTyZ9fhAhqnZEL2TWJXqJNiaDbz2hyAq5La1xex1/KBfvr5AYC0bg6jSZ3fJwb2yn/tunULoH9HJXLKImK5l+pzTHV5xJNovFxAzGsfuO8//2RCGZuSFS4zqazGzBsQ23uidXzNv1eg+3Rai6LS6gFDw4jKv3lHAVlj9p0Ko2UBeGR9/8nqB3Py5NHZxEjXy4nGo37Er1969R9fleibFq1SfpLEwGBF4gAUdkQrW7rC8kTstd99hDgu5Xnd1LpGW7w/tFEZWeOJ63v8rpdngholA2jI/Jx+dXKfvUR4DPzKD/DiQZdWS5cTN0dxIiG2gcabetyuxE9rqfn54OI8vJq4N9EUK2TO03265jMr+1ClCvjiHPczZX961DiUtYlOkhnAlG5vZcIZH608lrHAQWi13qrvI7L3v3NiEZfGSSbSDRGlmzw+aXyun0o//1N/vwSeb0u3O63VNJaFTg1/z+YaDjcRZNRYk3SqAZhJhDl7QyiMfOjMi06uHS6C/vuJKIudC9xCXjZmDmFCNaVc8ZtSJyzGs5/RgSXTsu/oxytvTTRoDmJKGOO6CA/qxPl0zuoa5wSjfPv5G29DTGSoeHd9uhbDn4gH0e7E+Vu2dgaQVwC9VPqgmz0vdHN+kQ94vPEcblqzuc8HTFLnKP/iwggLEUEWG8mLnm/lzYN6Mr+9mg+lm4DTpyDfCRqd5Msbw6wUF7H6+X1cwV979beKDg2IuehDCBsQ5z/y5FNk2gw51jdth1NjGStjmReigiwXl/3fQvEvER5Gz1O1FcnEGXx0dTaDQvl330/MTK32U0TFiM6AspRO3sRnYxl3XAD8lzHLbZRIo7tz+f8LEfUTe4iytJDKvvUmi1+01J1zw8ggmJlPb4MxJxf5q9JOrsS57Or8/cPIerMXybaDD+hs46bFcmjJ4n65RepzTu2CB1MFZHXRXlH8bfkfXkLosw7PC9fL++fL9Gk/pTf8yWiXvPhfOzsmH/bMvn3ja68v9FN1YYTHctfIQYCTKy89mVqI3r3JzpcGs2htkLe18pt8TbinFoG0kfT9y631eOwek47kFyuECPIqvMjX0PtnN0uGLsaUd7uA3yn7rXLmEdzcfqo2y4DnQEfc2nDRkDg6/n/RFQqniVfPlZ3wLeq+K9LPy93IEYwvTP//0HiBDOd6AlZue697eZtOpEc3MvPFycuB7iFJpfj1KUzrm69HEWl161aILcr1PJ7dqgUgmsRlcvLiRPiC9TN/1H32RWom3SS6I15lFoP01bEZUcv02DUVv7OVYgK1jO5MH2MaPidQ9/LPd9J80mYNyICA4sQIwR/SJwIH2U2Jm/t8nZ6kjjJHUce8UNU1p+ncifWvHzFus8vRVRqbydOyH8iKjcpf/4dlfeuRJNbLRMVqzcuu6Q2Km5ZooF0Mu3njViMaEAtQvQqljcUGEytIngscbnvAnU3GqIh9hx1dxAjLmF6ovL8eKIh2qfiT5O7P+b19TXgK5V990lqd1WrVlIup3ZX1XJ9Tsj7yB6V9N7dav3S5vKYAVi35SSsHyEun9yUaCReTlQsv0Bljqt5kJ9diV7MTciXy9W9Xq77D5J7/xuksR0RsF+XqBSfS55vg+jEeSOI0yIf/T4m8+eWJSrpd1ELiq1LNDzuIsqdHTpIZyhRaW9Yie5w3Q7P2/Pj+fmWxKivhyvvKW/C0OqYKY+PPYnz/V3EeaDjfZtoHPyESuONGGH39gbvPZQG57jK8b0xEWT8DLW5nw4jJkEezDwKvBAB2PLSrYXyfnIxMcpucCW/F9BiJAXRiFs1/79YXi8vEaPOFiMCk21HajdJ+0giKLYttct+JpFHgrf4XHlJ0luIQOjRxIjcM/L/fwD+hxwIq983iXP/mcTIrCFEEOpBYvRCtXNhrnfcEA3FzfK+fAYRGChHkm6Vt0/10vxm9cVViHJqaj/zM44o846BPvMjXkJ0Cn0dOKpNGhOIxvcXc1qn5P1xLWKkynfIwdkWaVwJXJP/X4qoW34iP1+c6IxsewnX/PQg2gXfJUYCXUdtBO8QYmTQtUQ9cjOijtFslNV4ol55KzFfXnlZ3fpEh8uDVEbjt9hnFiMCLpfnbfsXau2Wm6jUZ5t8vlr+H0Sco7bLzwdV9p9rqYyWa5QGEagbTNRX7wDOr7znMOCc2VjP5Q0gptHm7vBt0vlQXpd3l/s8cdfszxNl4FdoMq9vfu+iRD33eqJ8KtsBU4hpbsqR8WsRgbJZbn5EBPtOoDL/HHF+e7Gyzj5OlGGz3MipLq1982+ptpHGEnX0htu6bhuXN1v4Bn3nZT6WWa9YaXdH+wPJnVGV9foscS6ejAGyefoY8Az4mEsbNgqQn9B3Pos9iQr3wURD9Z2V1+ZZzyAxmup1aqNohhK9/m9cSlOfH2qNxKuoVESIaP1M6oItzX4P0Yj6SeW7V82F+l6V7x5feX+zdOon8n0/MYrpcKJHZU+iQTs7DZIjckFdnkx3zwX+43WFZqNer1PJd5MkemVeIq7/L2++cAARMOsoyJnz/n/AJ8v1T/SWN7x7TLe3U35tQl4flxKV/tWJCtKh+Td+Pb/vo3nd71j3+dHUbse9c16XV5EDmXn56nnZ/yNPeNkgH28lKrDlaJl1iIrG3UTg7kkqdwtt8XuGEYGNsUTlr5zPbVxOq2y4rEqTkYbz06OyDbckAgqjqE0auiJRUX1/PqaG0bgHr1EP+dLE5S+/BW6rLD+QCLrMctceonK1DHkUE9FTfzJx2dMi1HoIxzOAt66fzfU7iFojqAymDifK7sOYB5OXN9hObyUaIZ+kyeTExCUcTe9sSzTExxCj/b5D38uDhhMBgpaXi3TxmNyQ2qVoaxOBpI/l9LYgRiu0nLS+2bqag3VcjmLejmhsnJbz8ynics1HqczH0mq/yX/PIMrLhndDa5NGOULig3m9/oLa5Z7jiJEZ5fyTjxPlWaPz0kgiSD6qbvmA3JE3l0OfJwJZW1B3fiYCXe/pMK29qE2mfxfR2ff++uOyk/0hf/Y7xIiiwZ1+Lr9vRWLKisE5T//M+/9OxEi256nN37QWdXcUJ25G9BVqDddfUQv+nZj3vZ9SGRUxl7fRIKI+cx6VoCtxifPjREP4ffMiL/l718nH0kJE58Rn83rZgKi7Pk3fqwYazZO0KtGZcW7+THmjmvOIztWHaBAsoe95cVMiePMrog5dXo3xDWLk9Tcbfff8/CCCGUOp1QsXIs7l1wBnVd63Hi3mSMzr90ZqI6TWIzqUP0nUTR6mxc2bKumsRNwJciEikPQLok5/duWYaDcKaBGi3P1cfj64ckzvQNRVy3rKllTu5t4grcWJQFq1w/4kakHRTurRSxGdUR3NMd1oHyTOyw/ldVoGC9cjyo2L8t9yZNyKNLkEkBgM8BZq87JtSEy/Ud6VdA2iHLyDGFk8S8cUlSk3iLL8RWrnpvflbV12dI2iMj9h5fcsmo+bA/J3Hkgci+VVKg/SJKhO32NyMnBP5fm6xIjXzYnz5EmV11p1yk4jyth3EJfS/zH/vzS1O0Kv18n+56P/jwHPgI+5uHFr82scRlQ2tiEqqbcR89X8hMpk5vMgP+XJ4R3kkx5xAnoqFyYfJweFWqSxC9H42ZcI/PyAWYMtZcNgcxrcbSqvl4eJntZPEBWd8g6aK+VCqGGPToO0Pp3zfx5RIV01L/8BlcsqiMpV08kdiUtdH6V2ctmdGHX2NmJUQzVYN8uICqKX+SfEifZUolemDLQcSPTMlUON16dJY4++PR/3EI3YQUQvzSNEY3br6vqeW9spv1bOK7QMUQGtH6VyMHFCeX/OX6MTabnfbZP3s/KuMmsQQYaniRFySzb4bJnXKdTmdqqfL+Fwopex5WWrRG/+3fQdiTGeaPh+ppKnfxJBybajVeaXB7X59/Yst23eD69m1hEKreZfKO9k9OX8/BPE5RTVS7PfmOSdvpWU99B3hMMO9A3GTiFGrvwcuGKg19lsrNvytzYLRM2rUTdvzPWVt8HYvI53JwLPpxGN1l/T4pKTnMYaROVzqcqyIURDoOxBbnjHy24ek5X3r0PMS3MS0fhMRND8CzSZK3IureMReR/9dGXZWUSj+h206JFvsH5OpHYZ2GyPIsll01l5m47M6+X9RAPpLGLU1edpE8RnNgM9c3n9LpP3u7Pqli9EjAr6Kh0G7YjOryvy/8OJc+zrRGNpCJVzbKvfTpz7HyYaR3M02qeyzTcgOuoWJc7dL5DvaJePuX+Rb9JT9/llc57vrsv3e4nRZpdVls2LOcdGUrmElAj6PEicQ8vLCKfNw/0m5ePh8nxcjau89mRe72VH19tocPdLopG7GLVRaNU7KB8CXNLku6sdQJ8Crsv/H0ffSfjPa7RtF5QHESDbkAiKzTLimAhqf5Qmo+voG2z5A5VAEFEXqQbaNqb5CLTq+t4hH9PnzeZvGU+Uk++uLNsxL5uen29FnCv/QZO6HrW7zZd3+n47cfXDWOK8exS1jvRWZcwkoq7bcj7nNr9pdfpOI3Mdcb5ckig3lyWCYrcR54dmd4Uvy6pNiABQGRRbk6ivlR385UjBaU3S+RQRqKt2KP2SKPdGEO2KmUTbo9HxWO4vexHl3sFEOXMyMRJ062b7Rd3yK4mOkZOJKQz2pTY37gPECNFB1d/eJJ1diEtCz8/7yrI5jevztnuYORyN7GM29/WBzoCPubyBowAt5/E6NR+kK1Veu5kmc/zMpfwMIXoEDsn/P5ULg/Kk+Dtqd0t8azVvlQJ1IhHM+DHNgy3TqPRuNMjHWkRP+MdyIb8wcZ33tjkvD9DBZM65MB9Vye8LxLDrSyvvWY8IoP2R5rdhX4VawGlXoldwGnH5101EoGEVohf4xzSYi4fa3ct+Ra1X553E0OCykTmCOOE0nGS4ktYl5FuC53V0CRGg24EYjdbqcpPZ2U5b5+30jjb52Ru4qm7Z4Tn99fLz/YhgXsOGDdF7+LX8/0pEQPQG4kRevdvlWi3yMQq4r/K8nAj0x3n9txsyvR4xxP404kR6P1GJGJPT/j0xcnANosLdcjvNTw9i1MKz5JE6RKDs3srrl9PiBgNEeXAblcvH8vLTiYrCMGJkxZ1ExaXRhPxfJHovlyIuZ3iWaCROJQIOHyUqrV+u35/m1wfzQUChQZ7WJHrTXyIqqJ8kKnTvoXbJ3NbE6M79aXyji7IhmfL2uYvo9d+VaBDcRmW0U4vjul/HZP7MRsT5cYn8fAxRZl+S97tVaDDH3Vxat8tS16jo5DfUvX8RunjXRyIA9gUiYH9SXrY+UWlfs/K+tjdpGehHPv5nUus4SkSQ4XKicdTRKG+i/vQotfmMBlGra61InLeP7iCdVYgOra5dAkcEql+gVg9Yk7jxwYl176t2LIwizp/ljX0GEQ3NcyvvGVz9O4+21w+A71Web0iMkBpb9765fVwuT4ykL2/iNIQYdXJDfl7e/bXpjV6oXR1xSl2651G5m2a735TTuY0IjpTl6CnAZ+vet0BcVpnzOpmYPqE+aL0ycalveaXJzTS5JDCX1b8iz/WYj+1NiMumy2Nhe6IO8Xq78oqog99IbcR5x/sY0QH/JFFu3kSMFDuMWtDmp0R9fh1iJGSz4NiuOZ0r899ticDLM0S9+Bn63kWz7VUu/dxOE/O+96n8/GNEHXYYUa86gyYd3ZVtMowon8oRfmsS86o9SHQwlPOabU7U42bZj4k68lKVfeRzRHtnd6J+/UGi/ndSm7xcSW3O0hFEG/Rrlfds3Wgd1v0/nQhabkDUP64h6kW7EuXC/ZX3NhpRWt8uWou4kuWj1KZHOJp8GXY3t6ePBvvFQGfAxzza0NFT9V1qJ/Xy4JpnveGVvKyX/z4FXFtZvg4RqCgbVrsTFdhGEzlPphbxry9UphInx7e1yUc5EmMxYhTJLcRcbB8kghMn16XbKtpfzgkyjejN3Dk/Xz8XZp8kel9n0iLYQQQAXsm/fRHipDyO6M3+PNEz8zqVSyvrPr8uOaBHnDTvpxYU25booepkjq8NiBEZJxOXMm5DbQLnz9Ji8tDZ2E7b5O10QAfpjCEa4Xvk5x8mAo3XltuS6Km5sPKZ6olrkbz+diBOXJ+jdsepC6g1aC4iTmoN72JFNGKeIxpXaxKN/m8SDZtFiMZ1y9s+E/v5iUSgptw2o4nRbNVLZqfltGfp7ZpfH8RohZ8SlcEbqN2e/kt5v/08De5eVPn82/IxMrHBa5sTFciNqF0avG51n8r/X56Pm3I+rFF5nz2Nvje6uIMObyrio+G22jlvg/pLFVYkejh/mY+tC4gK6izleH7/4LwtPp6Pg4eJwOcXK2XGvkRlfJbgWDeOyTIf+e9qxKUzV1GbM3E80UCfZz229LPiS5cmticuU/oWcanpgcRlOXvl7fHhuu14BS0aRfPLg2jc/TiXFc8T59UD6OCGLA3S+RG1USBvIc7duxKNrAeYh/MA5jzsTNQhypHRZVCsOoVAw4Y+0bD9FDES5Zfk82kuRw8mAkPl+XtejBwbDXyr8nyznL8T8rG9EpURRP09ZjrIT1nPKwNTX6BWj7yFCH4c2Sov9J0jbTlqd29eOO+Dx1O5VLRSBlbrM+OoTNad97tb8/oZnve/PvWt+f2Rt2V1H51K1JkfIjrHjiTqFlu1SacMliRiZNjniLrWMKIOfAYR0G93p+ryBlnn0uCu8x3+plWIDvN3U+swLO9M+afKsXQcOThT9/lyG65DBH+qU1XcS9RttibKmXIi+/XoYGRxP7fTjMrzGcRAhy/l9Tymw3TK7bRO/i3lVUTvJ+puK1Teuyw5WNYkrS2IDtGvEOeq9Yk631WV9+xK43rlOPK8YkRw7svUrvy5AfgPTW6yUX9sEe2mq4mAVvWu1zcRHSkt5wHM+9kSeV2eSu0mL4vkvN1FnGPn2TQavfgY8Az4mEcbOippd1LrNak2JFP9AT4P8rMplbvJ5cLxOKIRNZJaL9juxMiEhqN4mDXYsml+/3/lgu40IrBzEU0u7yEuibir8vxRolevvKxxtfrva/G7PkBUlIcTDYlTicsPy0s1t82vj2ry+TWozZuzKVGhvZiYZ2AyUUko7xi2Es3vWPXO/Bt2y8+3IhqP78kF7zrtfk/O/8VEMKms/O1CVLobfm+LtBqN6PsVlTtLdpCfNandje8JouJ0Wz5RnE5ULt9bef9idZ9fjqgofT6vz6WI4OE38utnEr1HU2k9H8BbiQrx3cQJdQdqd/+6iNqlDZ3OEbMmMVKsGhRbiBi6fi9NJtue3x6VbbgJ0YAuh/9/kQiMbEgEW39Jg/mfKp/fmRgJWB9wOQz+f3tnHSZHlfXh95AEQggWXINL4MPdZfGgwR2COwEW2MVZ3N3d3d0dFmfRhWWRxd2d8/3xO5Wu6fRIkunpmcx5n6ee6a6urrldcuveI7/Dv0rvd0Ke9sLIX+7T+qLJy5TIyHEIFeNnYQC5jDZEhebS4rkuIlMWKM4B6scvpaTRgYxVz1I7tX1KZAwtnDZDKKVFxz1aaIfVTAVpr3sS9dvHoQF2D5S+uDUysk2J0tWHq+9r9Dmqw34HIl2Y80v3nqHn0yt0gaixaPPkyLC1Fk11gIbruKEJ6xQoQuRBZOB9HKUZrUSVEHMH/K4ZqDiQCqNYOUKpGFv1RoaHhWk62RuInBgnxvv94949CmmQPVSva6uV37Ukikw5hIgWQ4aH6ykZaOvchqLfuxKNN0ZHBq0LkTNmWjS2mLH6OzX2NREaG16HtJfGQxXTV0TjvN1aact58dsPpVJ5fnoUwfNZ9GMdZsRsx2M8UfTZz6OUyGNj/VrICFPWmOpX4/vlVPuz4poZM67zo9HzpYnmGM0/V6ahFV3d4fxtS8Tv2ggV2noROeFPoVKNuCWpiRWozOF2o6KV3AfNLc5DRuMOcU7EMf0QPXcPoI1GseI8oTHag8DjVZ9tjIxdfdH4rZBjGSaoIPbzFJqXnBHXz3So3yqMU3uhccqO1HB+xHm+FRkYi4rJl8V3No/XLd6P8Z1t4n4sDFp90P39Jnou3NaWezHaP0NpHxdG+8ZBTrp/Ff8jlzpc141uQC4deLKVZ/4UUR4aWbcXjtdNBoXRIdUUI69Du2ZDA64LKUV0xGdHImH0p4nokBrfrza23ICi4zZHHrjdkcFqIUoaWqXv90dGo0HIcHQaFePT8ujhtUhp++YGOmOhaKPlkBHmoNhXdeXJW2ghXa+03SIoFWkGNAC8iooHd9p4/zNVFRnj84Wo6AEsFsdl03g/QzwAitLNwxQ7KO1nAipGsZWAb1Eufy9kJNu9dA21Nuksp0+W27MWGsD8pbX9xIPhaSripaOjB/Jg9NCbBz18Lo/jvEqN33MOlXTindBD+BDkpZmHtj24tkCD2L8gT93fkIdqudI2rT640GT7V5rq0RWTzVnQBKtmJdGusCCj9BdV6y6lFWF0NAA8AHkKC8/iOHFu94hrtgfqO5oLb58STVKGGbAho++jtFINLJc2neNliLTW6BveKN2fPUrb7RP3aS2R9tWQN/QO5DjYorT+AUIQuZV2tNc9ORfSA9oIGeC3Lf39GzBfo495g85z2fB8F6GRRMXQsiCV59PYDGf0VVdfos86u/T+FuQQ2Dz6qv6lz+puWELRVr/QVCagbBTbHhl4PkLGmCZ6jfH3/9Bk80hCvxGlTK/Rgb/DkGHjYDRJnhiNafah4lh7hBpV7OrYpsmpRI9dgapfF+OkcaL/WL2l8x2/4yIqGkljx321NBrv3UWNMXjVedoeRafMi8aEg5HhchP0nLyjI85ROx/bydDYdgo05nwNjWevpWScQAbcZtPFUYrqIciwsAAyQu6JnOUHlbZbB8096hqFE7/hY5oWPbsQORh2i2vKkOGuZlXluDZejGOzOfBM6beehMbnh5S2r1vkWOx/c2RE/zvDWaSmtI85kNFrnRqfrRLneEz0fHm51nlCBuFlqGTDrIHmJoORQf/Z+D9jo3nCMNcMYVCL1xcAL1W142GaySSp2k9R8KtP7OdGKv3uHQxH4ZDYx9VoTlvMzZdC8/iJ63luu/PS8Abk0sEnXJboosLZSkgssCxyOhp6yF6NQn3rOglAxqR90YNwzqrPTgfejNdFFayaaWXxwPgUPSxXoFTNrrTNHCgiaBijFDJCnIss+wdGh7Q8GmTcGJ/t2obf0w95hY5D6ZPVRrF5opPuPxzHaAqUGnRavJ8GGd1eQIPvZlPtSsdl06r1syLP1ZJtbMPK6AG4QrTnLDQgPDqOWZsGFejh9SlRLRUNvB+Ih9XLtEFTi4rhqFwowJAWwY1owDxTtPX16rZRMdBNhTwvjyAD5jy0oskR98ea6CE5qPSbLqT0sEUTiSdpRcgZefS/oFI1rDBQT4BSfI6p5/1XzwUNCj6nqchwUWl0AJpAFN65WhOHvihSZ7PifKPB/lXUMHA304YzKOk7xLrp0WTidLpIdEtXWZCX9rB4PSZNy6qvS41qVTT18F9OJS1tJeRNXoemUQLVlQTb9Z4sbX8QEVWAJp0voMnqjGhSO21b9jOqLSiC7rmqdT3j+L6AnEuPomjeblNmPq6Lt5FBdVU02ftHfLYEcrysWNq+3ml/MyKnSxE1XqRPjhHn6Ew02VwORSgNZNjUoK2oFEIZgp6nD1KVklR9T9bp90yIDEbzIIPfLkgTbX9UIGJQ1fYdYbRbnaYT6OL5PUUc011b+X6R7lekX24I7B6vm+2napynvsjJdzOVSO3R0Bhvznofhzoc17JDZU3gN+Ce0roNkaHse1qpIoueK+fH9Ts2yqbYCGnS7oaqGv4Q/Vfdrpm4Hz+mqaPmaEJfFxmrN4m2XNRcW+J4/BPNTbZHkYY7oZTPfZDzcOr4rTtQRwMKmkecTYzjRmI/RUGKtarWb4DGwcWYf2WUelyzEFFsMy8aC/wLGUP3oiLPUxzfi1s4vmegiuiFMWtqZGQ7rMa2zTllR4978eZSn7BsnLe5mttHjf3PSanaLHL6XUTFcdFiamYuI3g9NroBuTT4ApBuz5B4vTXKv38OWajvZiSqmAxHG+agNIGKdachg8t4VduN18w+lgY2j9ebAefF66LjWASlyv1J84L8E8eDZfTogB5EUWT90EDnP9TIUa+xn4li2wOr1k+KJvbn00b9F+RFego4Id5PHZ39zdG+lVv5/tLoQTpa/Ib5UGTFivHg+B+KIBsNGf8mbWY/K1Dxzh6ChCALMeoVkSe3LWXpl6ZiFOtBpTrdGCgs+xhCk6yNx6d46CyIBsnrVX2+fKwfRp8ATVb+RAaS+dr6cEEGtdVK748ijJal4/MAzZSsrrG/wki5GTIUzosmWOUCDl0mFSLae3b8hgHIyL5JLP8kvL4oaus6KhO3WsaxJVE/MHe8nxtF0bUmmmtVbbkBGcQ2iGvs4up7py3Xby6tnvddqGgzFhERu7V0fqrWF4VMVov7ttooVtzvo6OJTJFu0K73ZHxnIDIEFLo/DyFj3WgocuxCKumf3ebaQdEJg+JY7ISMErdHX3oLis5egzZERY9qCzKg7o2qPxYpYEtFP/cE0prZvQPbM0f0wzvH+95osnw1MggUKT8rIKPxBFXfXw+4sfR+I2SI6o8iK1eiMoFsk7OiHX7TECpOwnHQs/MWKk6WhTvw+E4T/3/ReL7sgCLqtkLjmyfQ2KKlye/0sY+/RF/zGsOpD4iMm49TcXqPiwwIZ1dt1yX6KULehcr4/Vwq44TNkFF3l1g/RSv7miT6pClQhPkXSGt3SxSF+Eicr/E64HfNijIe9kPzkS/i/PdG0VfXAF9SyiCodf6Q43cWNDfZAWVBFHq1E6MIp4mQMa7mmL4df1O7GGXQeHBnNE8qxPfHQs/ZfZFjdAxkIB+NquCCuGZGR+nFxbh+mljXE0WPtXh8Yz/TUTFETY7mS5chI9WEcezLMhHNjWV2pzJe+QtyFG9L7fFMW7JUVoxjsRMlKR6aGRPlMhLXYqMbkEuDTnzTtIg1UBju+8jAMAWaQB5KBxjGarTtZDQZLkTbW0uhqRaQ/Ttweun9/NGxnom8D3O1tC80kH0Bpe+Uw+OvbWungx7A/6SSErQomlzdSwuV+WrsZ2oqopRToQfgKcjQV0RW9ECelAspRUdReYiOEw+LT6LjvBYZADdBg9seaKBbU6CapkaGsVE48JKldedRKbE8ZvV3mtnPOlRSJ4+gYizr09K5baZ9hXj336rWFb9/A2oUmUBGuQeRIWqEH+7I+PZEnJPzkaFvFjRAWRU9kBdv6fegSKrrkLfvPuDwjrjX6rWgwUXxwF4TGXKPRIbRCeOYzU+lHHmzKY1xDJ9HxoiLUVRgzUkY8pwXRSLKfdwJyED2LkqnWKL0WY+qfdRM2c6lTed9STQRXyTer4PEa6dAg//laUWzJa6PR1FUQHNGsYvQRPC5Wv3WyN6Tpb5jftTfvoWMQT2QB/pB1I8+QMkg112WuL/PQ1FQA9Hk7AZqPKub6/NG1SWOTRE1uTiaoBWVPPugaIapStvXO3Js7niuzIc0cE5E44Xd4jou9LrGa+b7FyDDZyH8vSoaPzyPogLvintye+I5XuffMzPSstsBRXecS0SaoGj/p4mJaxzvkYpmaUN7ZkQRquujMdgWcbz3QVH1ZeHuI6gRpY8cYo9Fn7LLiLYj/vaN43AcTZ9z5fFXq8VIOssSffWbyCiyVhzjQWhMcWhpu/loxnFNxXg7KzJYzIeMHvfEfTBO1fb1jBybLc7NsVS0frdD4+rtkeO7mHdsQguyE9HX3EipoFH8tuMIZ3qs6xKOVWTAWhX4e2ndbtHHFHOLHsj58DE19D6Rc6JcvGJc1O8N1/FFBrrt0DNufjR+eA3NwV6gFeNl6f0qaNy6A037/fJ45grg5Gb2NxrKILkcOQXKRrGepX1cTAtjolyG4zpsdANyadCJr2j0PIA0f85BhrHZkZX9fSpVVHogz+O8HdCuM5HmRWEUG+4QfRQdUHhrF0aDkROQh+ak0naHU0MEHxmJ1ih1xL3RAPCy4WzH7CjK4AYUJnwasPQIHpcp4zydHB3l3+OczY0MKg+jtLVjqRFmjAZub1Nl2Ivf9jRwYWldS+KfkyPj3mzxfuf4XbPEg+QIIv22+gFRtZ+/IC2iBVAU3alxnU2LJr3HEBE9Le2ntL+zS9drr6rP+jf32xhJwy9NJ9BPoof1AGQguABFnKyIBvI1tTBK+xorzsdE1fvvqguV9J1Cj2U6lAZzAppYrY2M1pu3cnyXQQOkPQjtIkp9AzKmzI8GiUOoYRyL99UFGYr29UApOvuiaIpukwJWh3Ne6IJsjzQQN4v1K6AI1TtooTJpbDspzRvFLkUT4jFQBMtLlHQW2/OejP0sQiXdZQjqg9cs/Y+b6GZ6WvHbRyudl+VQivMYI9unjioLcoZdjAz6xURsEBKr3pWOrXA6EZrkXR/XfnHehhAGzar+dFyaOhYORxHW46KIsSFUoniPRGOAk9Dkr+6RY8ipdSoq2lHci4cibdnTkdFkGeT0eip+f70NkGfT1BjxJaHLFu8PQxpQNSOcUGTTpLRBx6jqe2WD1xiEUQSNsYYR30eGh4cpOY8784IMj/Og58qlSBN2ZTQG7o3mJoNRwYE/49pozgFZPBumRQUL9kBRVxOguULN6oN1/G09UKbO7fE7Vgaujc82Rk7StWhmTB7H5TMqkZ8Lxb16dPyuRRmOKOnOsKCIrFeAteP97MixNV70U4fF/f0hrejvxj1VGKprHd/7qaFtFp+PiyRjBqKghA+pZCVNjQxQs7d2naAosb1p3qB1Keqz7qIU5V5jP/1palgbGqkb3z8z3m+J5pudvjp0Z10a3oBcGnwB6EFc5G8PRBEVVxIW97jp5kSerJ9pw0RiJNuzFCNhFIvvLYMGbgtGJ3ocGjAcSCU3+0Sk9VWzOmRpX73RwPHaEWzLeHGMJyUMbSNxnk5Euj2zobLgV6GH6sVoQv93aohLUxkMDKQUnRO/7bFSh9qXNngaUATQq2hgcTsaaM2JIjM+RYaFtkw4ByLjyIXIi7cQeuCdilI0XqFU9KCZfVj8jquoErWsvn7QwOpU6iSyigbhhcHwMGQkfAFNnhdEHp++tK5j1mqFzq62xHmyeGifHOumRw/w21s7zzX2V9aXWxbpQV2IqmEWk76hZdub2UfZ2HI0MsYehoxsGwDHNfq4ddUFRcrsQ0Wke4U4z8fEZ29To2hIM/sqDyJPQ4b5oTqD6HlVM5J3ZO9JKhPL8VCk2HPAvqXPd0TOpQ5JI+usC6r0O0KRLqPqgp7Ze1JxsK2HIg//iiZpLxFG4w5qT3+UPrlZvC+eM0Xa0uhonPR3NC5YimEdCzMh8e+5SusKLZ3NqBgAO8KoMAmVyfOByEFY3Ou9kSHwXkoVOevcntmR0X9XZHi6n4jCQ6lzn9AGo/HIHjvkLBiqF1fu26IfPh+l7d1JSa6hMy/RR5+FUh5Hi/afjTIFdkYOjw+Bvduwr5mQsXSvuCc2iGfC/nHOOqQfi9+xCTL6FBWYr6Ci+3crctC3plO7bJzzpZAUznEoS2R+ZFg7nTY4qzvTguY4zyAH2L1UKuQegtJf/4qcoIXY/jBzxTi+G6NU2VrH9zYqkaYDKKVHlvZR7H8XIouDynjkUqqKYzR3fClVT2XY8cx9pc9uAg5uw/GpNqw9GcepGNOug+ZpXeJ8d7al4Q3IpXMsNE17eouK8WQulDJxAKoE9AKt6FqNRBvKnq+REnNFlW32j461Pwq/fwxF5RwWD8BCk2IQzVQri4707kafn6o2zRC/bfro4A9FhrG26J8thwRje6M0irNjfe84Jq9R8QS3lGI0Axrcj46igP6BJpkTR7uepYUUhtJ+pkCerZ5xfspC7ccAe5Xer0EzHiI0KH0ZhUyPUVo/AZqIX41Sq76gzgMfZFh5Ghg/jsczyBu0T3PXe3dZ4ly8g4zXm6BB+l7x2TJocDdvW48PSpm7AOkuzFVa/w/kNW/RGI0MK+eiAcq8VdfOVXSRiUNnXEr3+Cpo0rpj6bMJY1msvG0L+xoDRZJegoxTO6MokG2RgaF/K98f4XsyvnsI0i27mEqa1qro2bhiW6/XUXWJ87tZo9vRWRdkNPo3pSgxlKLb5gpl7dSOAfGcrXYiTRPX+BnAf1HGwDDR7SgCpTyR2zLuifWoGKs7NG0LjX3uoWIUKyaNc6Pozv7xviMi2aZERrEXqFSbPAppzj1PJXK6RzzrWq2SOxz/exghcJoaxQYiLbZLSuuupQW9pc60UKn22TuuuQfiupsZpVoOKW37MM2kuKNon93Qs38OpB/1MnJyGDKkzFvavm7RwCgSaal4PQF6xh1MGG3a+r/RM+0QNI4ZB42zLkcZONuicX1dq27W4dhMj8b8y8T7vZExtIhO3inWTxjnbBiJhhaO7+1EpBiaO/wTZdxM1cx9dCBNK5kejPrRIuJvuervVL+O99UGrYvRWKTQgNwwzmGzxQVK+xoDjX1PRdFtxxRtKj5v9DnsqkvDG5BL51niQXEVcHS8nx1NXI8vdRDLIa/YMJpNnW1BegJFiPGS0bmdGA/N/rF+XRR+fRs1ShvTSkRZB/+e6k525jg3+9d6KLSyrwepVJvqg6Iuno7jcR/NlIuusZ9pkEfqImDCWDcXSldaj4hAq257M+fq+tL7iZGHcI3SeXqOFoyyyMt0M3qYzovCzB+Mh9jfkAf/yuE9ViNwnsZHA/UF4v3UaEK/bLx/hDYaBEbFBRnAzkCDtUKo/RxkuDwZGVVXbcN+pkKTjh1oGqa+H0rrWZUWPPRoELwLmqBOX1pfeNqnRFV7zx7e35jL0GM5IZqMDala3wt5bp+jFY2uOE+Xo4jAMeJ++hENCi+jDZqNI3NPogH1ocgIuwwqrHJhtH0L6ixu3BUWhqPKcnda4todGxlKiuiEseIZdSZtrArdzm36v7je+6D0se1QOtbuSANyDRRNU7PvRJO5c9A48AoUvTOMUQzJJKzdAb9nSmroQkbbzq9aNzUl3a06tWcyKmOhQ1H6eC/kCHoEOQMHxDPqZOqkB0TTCfpANEbbC03Edyl91qVS7YpzHn8nRxGMB5U++ydyljxFM8YgKvOZU6lUjr0TOTqOQOPPvmjcuSt1NjJEPzEE+Cbur9UZToMcGkOPF/fwxfFsKpz/WyPDWZfQGqv6XT3jnJyBskp6xfN3OvRsfoCSsbcNx/dSKunXC6KAgEeAy1s5ts+izIJzUcbMeCgC89HY51pt/D2FHtjJyIjZH8k9XIKqrxYGu3Fb+T2XozHQNKX1m1GJUi3+drs5xkhfc41uQC6dZ4mb9HgUgjsgOp8TqraZIh44bUqD6QwLGgDeisr03kDFKLYT8o6+QRdLA4lzdAYyAgwYge8X1dTGik66bJT6OzKQtipIHw+My6kI8y6ODGsXo+jDl2mDkQ1FoL2OvLwLImPHjcgTskY8/NaghvGyaj9jooH+pSgi5A8q1XouoGqgXMfzszqq+rlq1fpHkGfqYIajCMOotlARoO2FDLtPlT5bMI5Tc1osxaB2SWQ8na302R7x3RXK2zazn14oenQ1hk0Zmi7adQUymrc48MqlxXM9den1THF/noi0jiZCBvkWBbKRk6Y/lYiLO0v9+PjxXJq1pXM+MvckpXQWNOFfE3m0c9DZ9Djl8ah9XPrG38IodjoNHHNEOw5EE7xT0NioEOF/gFIl2Xg2z0RTQ8uW0S/WjBRDzrEjUdRuizpAdfp9a6KokCIVaiCKLHsVOcnG7YA27I8ixcq6QDuh8eapKKp5XmTcr0sWRvzPddGku0hDnAKJdM9Ztd1IZWk04BzPjIxiB5fWPQdcE68HIIP0tC3sYz+aVmy9Go19+6Lx5/eUDA91/C2GxhrfIodemyuzV+1n/Gj/9jR1Fr4A/LXR52wEf9NEKBpquXi/XZybsZFRq1XHZen4fg+sHuvmj++vjea9Q2J9X2pUPY/rbScU1dUnztOlaL60GJrrbFr+n820ZWMkWTRFad1OsfxfvJ8cpQY361hAY6LC8FmWFjmaSgpqXaWPRtWl4Q3IpXMtyAI/HzIgHVBaX0RRLBKd0gh13A36TeMjfYenSh3JjkjDqqjgUnRIs1FVsrwzLsijtRdtSJ9sYR+jxUP0ztK6uZGxYYvh2E8hYL4UisrYjor4+VFUpSu1sJ/VULGCa5A3ZWrk/XqQire9mBi3ZPCYFg2MJ6NSdGA3VJigMODVzXMGTVLI9qeiMfN03Fd9UDTbh3RjkXcqYeXnAAvF6+K6mZEWBurIW3cfsHFp3X5ownEdcFvV9jdSFf2JBHtvpMozG9fdYUREZaw7nC7oWe9sC0qBvAt5Np+MvuI2hqMSKBqEfoCM4IOQkWwXNKGvKWjeXvdkdb/RUj+USy7FQkVjcSByGu3cCdo0E5WI+oHxnGwSZYUmX4ehiIaVaGocW5yKpljZKDY3eu4fTAsVwOt1nOPv1sgAthyKNnkXRcQV1XJvpM4Gu3iGTVbVrgHR3xxEZSwyP4pGWb5O7dgu+t1JqtYviJxLm5faN8LVuTvyHCOn1pPAUaX1DwEfld4vgjSbxqnut0vbDEDGtG1K+x4NOVHeQY6U7UvbD6ND1Y6/a1aUPtmi87cN+5mSpkax54Fz4/VUyKDTbKXLzrigZ30xzxiCJFNuBy4YzuNbaBLOh4xiO6AotMepOAa2QllF/WjeuLUVisBci0pk1v7R7/QvX6vNfP9sIkIN9bGPUIkUmzyuySNG4DhdhwIAFkeOx393tXPdGZaGNyCXzrdEx/oFVaW3UVrMJXFTd6mQXGS06R+vt0WaBPMhj+fDKFd8ZmQkO44u4D2jHap/0VRL4f+icz86zvU0yPNbRJc1K2KOjI8PIwNU7xhgDIhrpXgY9W1uP1QGZlNS8UAvhwY7g9BgZUzk2VmupfZUf4Ym0j8gL0qfDjo3VvX3ZeCq0ufTI+9kXUvJd+YlzulYRPpr1WeTogHLulSMZuVzOglRSCPeH4qiMJaM98dQqT50G/Bkjf+9LlVpLMgoVujljYuMz0WF25EasHbnJfqDMZFRrCjsMhfwNaXqaIS+YQv76YmEdw9AE8oX0SByp7gmXqAZD3/ek7k0eol+bbtGt6OqTYWzaSlkHCsirOdAxqWjkHHlGWRYrk41LutYFUaxw6rvIzrI6BJt+Cr6llNQ1Foh6bAgqtbY4VIgqKrn0SiSrl/VZ2+gaKE2SViMwP8ep+r9Wkhf7ELkiLyXrjemn7D0+ilUcfU6ZHTcOMYEJ6GIo61pxkCAxr03EJp6KNrqLSqV1g+O9c8C9zT6dw/nMXoDOC9e743GNTfGs2/ZRrdvBH7P9Mgp9g1wygjuY94YM+wQ7xeJfmIcFAl7Ly3osSGn7OmoaENRKG5BFHV+dlxLNR0fNHUgXIKceq8Dh8W6ydAY5qiq77Ule2cVNH7pQyWQ5QRCPiKX4bhGGt2AXDrngowaz6KoimmRvsolKCKoS4n60XRCvRPwERXP4W7IODYtGsydTjcsc4uqSh6ODAqzImPS/1D02Hu0Tcdnaioe5FnjIXEy8u6NiyazK1Wfk2b2tSHSilg93k+EouTmRF68lduyn9hmPlopBlDnY7socFPp/UyoWtkwhrq2/J5RbUGRCG8RpdJjkHEy0iBbH0V2rlT1nTGIyB6U6nMO8pD1KW1zJPALpcEsJYM3CsUvp8hNhwbTV8QAZepox1xV/7tLTSA60xKDt3uiH34SCc0W6U6PRt/cWkrk/HFd7Bz7mQxNjAah1IaZ2tCOvCdzyaW0oEnhs8hZMT1KETqdMCig6OuhUfc1vj8XGkMciqIe5kIT8aGOVDrAOIYcevcjTaVxqTjkFkGT6m0pOTk64v5GWm6nIafLhFWfPYAm0/PFs26hOrdlZjSBXgWYONZdQIy1uspCxcHxQNUz/mykFfY3FB01CTJwtVQMamzkLFseGSoWqvr8MuCJ0vuuEFm3KHBjvF4XOZWXjPczoYirLpWxEM/69xiJiuFIkmHLOOc9kGN2P5Sx8iAlIf1Wrpdi3LIAchrsHu8nj360ZgovTY1jSwMLx+sJkcHu6NLnW7W170TG7pNL7xdA46kBcc/XrNydy7DLaCRJDdz9VZTuUlTe2h5FkQ10918a2bbhxaOXCF5GaXmPm1l/9LsmQNb/CZGl/u0GNLPR/IAivc5FA+MLkc7ItshItaKZzdPSDtz9PXf/2cwGoEHJ5+6+S+x3HCR8f7iZrVh1TmrxNjLS3WFmW6FIk8vQIGdTYB0z61Psx8x619qJmfVEA4S73P09M+vwPs/dH3P31aM9s6B7ag7k2VzAzI4ws5PMbGl3dzOzjm5jI3H3O9B9OEmcn0WQCOkfSANwXWBbMxuvODbu/ou7vxyv3wL2Ae539x9Lu54PRQQtB2Bm47j776V9fOfu35e2nxtFqe3t7h+hycwXwM9mtr2Z3R7f+7M+R2LUxszM3W9DUakbA58C+7r7d2b2KEpdOBQ4oegjat0L7v60u1+JPLdjxbnaADk2JgLeNLN+ZjZt8X9r7CPvySQJzKwPlQJFo6Nn/pRIv+e5uHfvR6nGfWp8f2pkcPoNuMHdP0SGoAdRGuNNZjaau/9R79/i7u+gqLcD3P0bd//ezBZGjt2D3P0sYCsz2yK274j7+z9IzuNkd/+8WGlmDwC/ufua7v4MetbNambT1LEtY6Hz9Iy7fxrrpgd6lY9DI8ZKw0NpDLlJ8YyP9du4+2B0vmdG48bv3f29Fvb1HeDIiHCduz9ZfBbPpmWB+81s3di+7tfxyBLPuDXi7bIoNfSJeD87GhcvY2ZjdnzrRox41s/n7nuMxD7eRSmY3yEH63fAryhI4gh3v6cN+/guxi3zIuP/Ze5+Qny8NfC2u/+3me/+WRqDPuDuT5jZlMhheKu7/xWG9slTAze3se/8BFjCzJY1szWRTM6u6LzvCPzVzOZq7bclZMRYLi0vKP1lXCI9rtHtGcnfUp0CsASqrLQn8jJN0+g2Nvj4jBd/r6ESZtwDPUD/DdxNG1JM0UP3jHh9HvKGPoIig1ZEGitFSeEJaEGvDg3On0QRbH2RJ+Z55M02NBE+GHlLmoswmZJOEDaOJhR7xfGdAokwf4aMQtshb3HdRHg7+xLnsw8avM1GRZj9RmD/4dzX/URBCWBlJPZ+MaHj0sK10i/+zoGiI65CEaUPxXXca0R+Wy7DHOeZiWqOyOP/YOmzndCkpugjFqaGHhCKYHmZSjXJ8ZE3eHvkHR8aaUgz3ta8J3PJRUs8i5dAkbZHUZXWjFLOngdmbOb7a1NKTUcG8L3i9TGUhKCb63/r9LsWR1FA25XWTRfjig5La2VYfcIHkMOuvG50lCr+KRFJUod2rFH+v3GuL0NjvWWIVPf4rNNHRpXaWo4Gnzyuv2uAWYZjHzOjqMK5SufobmRMnBB4nzqlu7bzsShnyfRB85xCgmSJGNf8nRoi891pQZFjf8b4brjnCCjTaKfS+71QteEi+6FN/RyKJD0djYGXjfHLichw91fg8DbuZ7W45q+P15OhKN77UFrx/UTKcC7NLz1JkhZw959QZZ0uj0fPUWI7pDfxFup43unwRnUufg5Pxh/IywsyjK6DNNk2cvffW9uJu79sZjuY2WQohH1dJPp5MTrWz7v7H2Y2ERpMz2Rm+3vT6J2Cvihs+UOX5/dhlCpxITLY3o9Sew9soT3/Q2mhDcXdfzSzu5EW2y/oYbiyuz8NYGavAYuY2T3u/lsDm9oQ3N3DS/0pClN/JTy0hwEfmVkP4M/iPo4ohmEiD+MacXdfy8x2RqmWX6DBwdlmtp67P1X1HXPxpZlNiAYmc6Br9kM0QDkcRbWt5e4nx/dmcffX63JARlHMrKe7vxGvb0Dp0XOVNukJ/Dv6iMVROuvxZnZp+Xy7+/1mtg9wupnt5O4PA1+Z2exocrMpcK2ZbeARAWBmE3opYiPvySQR7v6Fmb2Kosavcvfni8/MbGKU9vMC0tKh9FnRd15rZpOY2aVobHUVsLuZ9XL3var+l5tZD69j5E2MZcYg+g93PzsiL/5097fNbFfgRjP71d3Pr1c7CrwUaWxmzyGh+FVK6yZEE/VZgC9RGlQ92nGjmQ0ys1tRJP+H6Nm2BIr0n8TM/g/Yoyv1ecXYNKJvNkbPlH3c/c3h2McbZraAu39gZk8CX7r7yrHf8VAE5Mft3PR2pzRGGoyupf2AI8xsWSQ5cDqKjPswthut6vocw7tYZtAI8jCK1DrE3R8b3i+7osJOBTCzA5FB9iaUEllrztncfvaLfUxIpTDcd8AtyBnxR3Pj3YL4/GYzu92VGTEJcjJ6/L6HzGxWYPzok39rbZ/dlTSMJd2ZC5FI6+GuEN1uSwwil0MCsIcBp0SK0fLIiznI3b+OSe3vpe/NjwYP/ynvLwa+fVH02CwosuNilPJ0YoRvD0ST4qeaMYrh7q+b2d7ASWY2MzLQHRkDl9NRSsCfZrZMTJQnQelVnTId1t1fBDCz6YB/uvvTpjTQP+LB9Vw8sHq70lK71YMrjJ/nA2eZ2d/d/VYz20Ef+R9m1gudc4AJzex35Cn+rLSbvdz9KTMbH6VGng887u4/mdlCKKroKQAzWwD4xBVeX7ThczO7C7jX3U8zs38gA+x3yPN6nZnNGAPupc1scleaUdIGShOYF6gIZPczs49QZOgWKNVpdpRaeaG7X1K9n7g3bjWz19z9P2Y2Z9xftyPv77NmtikxkTGlJ51tZiuX+7C8J5NERN+3FnC+mb3t7heY2YIoanxlVIjkq6rvlI3Vn0SffDKwBzJOHx/ji12pRAVvGf15kwl5O/8WR86+JYCJzOxvyAnXA0VSfIwigqatx/9vDpOEx9vuvnZp3QTIKGZoIvs06hvb+38XRsxNzGxpdJgejNcrI13PjVC1vm2pTPrrasRsL+LcroeibrZ3ySwMFzG2nBKNC4p0++nQ8fkNeMfkwPOSAaqzPhOeQtUTd0ei8ouhec+VxZynPKY3s7GQfue0ZnaUK91wlMXd3zKz1d3955HZT9y//dD48AFXQElbv1u+duZHGtdrxWfzoQjPtWJOZaVrrsk9WdrHHzH32g05Bq5198fjsw1RoMueZna2u985Yr941MY6572cJPWl6GDMbHR3/7XR7ekMmNkMaFK5C0qrWA8N1DZ292+qO2IzWwSF7DoS9fx3jX2uigxgs6NItENR5M7mKMXhcuA+V959s4NkU278NsAV7v6ImZ2FRLaXNrPFUCny68LwMadLR6TTYtJhuwTY1qUtQhj15kXHZWzgzpj4d9ZBV7tTui9XRYO4M5Eo7t6UojpNkUSboojG/qjc+mtV+/oLsKu7rxrv50HG2V3CiLooSiE5wN0vLv//0j76IzH+R1FK7i3ufoWZrQz8n7sfFdtN7uF9TVonjutx7r523LNXoJLpkwJHoIjVc4Efkc7cze5+Z3P3gplNjtLhD0JRLQ+iNKmX4/MDUBW2t5sbBOc9mSQijNJnoOjdAeh+utHd72rhO+VJ29lID/R1lEZ5FxVJhdVRytqq9TKK1Whbf+BW1JdsjIICenpFY6thmFk/VORqIhSNtyTSQnvaFKW0IvCBuz/STv9v6DgrDJYrICH+z1Fk3XdmNj0qdjQZOu9/diHj2JSo0mq7OEfNbEZ0jBYm0g+RjpQhQf7OPtacE6WV9kFFih5390/is56u6KLRkCF7fHRvHuvSvmriCE+aJ4yKf7SDke0sZLwaH2U7HIYCC1ZCUZA/Iufvny2dHzObCUmDFNHy1yPj7qFo3lZI5jw8Mu0dJfFOkM+ZSy65dI4FGbAeQGV+T6FSjrhH1XaLoHDhgWig+xRVFW6oGN5nQXoeRTnsLZH22EDU+b+END9arPYHTBR/90Phz/1Kn02JJsSTNvoYDsexXg2FTP8fSmnYM47jjWhA+iKweKPb2YDjUlw3Y5bW7YPC3scqXUOHosnE+nEtzlq1n0ITZ1bkQT4bpYeADB0vovRg0GBwzPL/j9dDkAbF5oR+DtJhuQHYN67t3YFNutK119kWFDk6I9JymQVNxLdF0RyLolTo6VvZx+pxHeyBjO9zxvr90EBzxqrth9H/yHsyl1y0xLN5ciSD0KeN3ylXXJsk/h4O/KO0vgdNdUbnRkayev+eWVCl6w1rtLUh+rlxjA9AqftLI2fQIDQBXhylUv2CDHrj1qkNGyPjZfFsXRGN1/6BJuW3000rMSMD6tYoPW4GYCmUZbJlnK+nkPh/w9vayu8YA0mOlNcV1Q5HQ9Xnz0AZIuPF+nmRM6nVKs+5tMs5KvdHeyOnwiDk+N0bjTlXR1phtxKaesX5amXfA1HE4OTIObEMsCawZqN/d2dcMmIsSZImmNnYLq9h4U2qjhRbFJX37uXuReW33dDA6rBm9jmBS8NkW2Ae4CF3v9zMNgDmdfc9Y7sWUyvMbAxksLvb3a+NdVOjksv3eEnLpDNHdZQio2ZFXvkxkeHmCiT8vR/y1PfySrWbbk1EMbzt0oU6EgnpzxOfHYiO45nlc25mg1Bq3sQopfI2NDg4A4mbPoR0GL5CnvFXa/zfCV0pRoYGw7uiyLFzzewqpFO4o6tSYjIc1IjQWxqJQZ8DXO7uP5jZ6CgdZF9vRgeydD8tjwb3z7oi+/ZHkaYLuvuHERXrHqnf1dETeU8mychR454+EaWl3xrv90XFchZC99PqyGi1t7t/UOe2zYb6/SHu/q96/q+2ENFiQ1B/dUM8T+ZChqknUN/1AnCuV9Kh6tGO0d39V5NcxTnAv5AA/aImHcfv3P20ev3/zkxEI0+LzsPjSLfsjvhsG6Cvux/fuBaOHBFNPTlwHPDfGPPPj4yi3yJN3409tVTrTlXU7Zzu/qKZbYKM1zt6pAab2akoJfYjlOHzrLtf3cJ+lwLWdfcd4h6/HDkklkVj4F7u/ktnnjN1JJ26HG+SJB2Ph66AR4iuD5s+eTJ6aF5kZreYhDpPROKei5qEeqv3+UWEuC8EPOrul8dHy1IpIQ16QLfUtl+QEewYM+sRKRK3o3LJe5nZFGa2kpnNFpPcepdhHyGKh4+7v+buX6CItx9cWkobAMfG3yaDd+vkJdTrQekcfouieEDG0XfMbHB8fiRKizwr0t+KQcZ16Dj+xd3PRMf5dOBUV/rkFmhgMWm1Uaw41mEUGx150ndGaX3nmtnWKOXvKTRwwcxmjclX0gZqDMJ+QimUV7j7D7HuLFSE412aoXQ/3e3ue4ZRbD9gK2A+YHozOw0ZR6816Q4B7G8qApL3ZJK0AzXu6V+Bjc1s/DBUb4J0rCZABrJJqOiG1vV57e6voIixhhvFANz9S/TbbzCzSZFB/jYkwD0x0lQ9E0W6tTvF8faKnEhP4F133xG4yczuRP3nH1Xf61GP9nRG3P1DlzD7OKhw1B2lj1cEhklli7Fppxx7QuW8m7TT+iJDSWEUmxONp25z6eCdDlxm0mxN6kh5zuKhfYqyGS5y6aGNGZ/thPqGzZAd58dWdv09sJCZze0qfLQqyrT4F+pfLjGzgZ15ztSR5IAuSZI2YdLyugA4z90vcveDkEjsEjFQWh5NRDeu9X1Xdcj9YqKJmW2G0pOuDwPHEcCDZjZOc51zGDtuRToc4yBR9EuQqPauyNu5FZr8rtqFvB/fAAPMbBGXEPzKKHR6qG5bEd1iZjOEkbFbEA/rMVBY+fyx+iP0UF/YxS/AFKhS6djF9+Lvd+7+bRhDLgZOdvfzzWxjlMpyPDBfDBLL//fP0utfUarfP939PFNBgDmRxsjKyMCyI4oqmtikN5EMJy49jHM9inGY2XkolWCNtg7aYlIyI0pxXRpFfm2OvP0boSq5e5nZScDUyCNei7wnk2QEKU3w/oomZscjR9hqyAC+AzL+nOqqZF1EbNbV6OJRqKWzTAC9UnjoE2BPdx+C9DMXRsfsWa+TzlON8dFESOcRdz8aOR2nAK4ys7nNbJXo8/7oTsaxEnOY2aZm1t9UfXV8dz/ZzLY2s13NbAsY6kwep+VdNY7SeV8NyRc8H0axeZAD6DN3Pym2uRd4owHN7JbUuCd/ANaOz34CCCP6zkg37iavROPWLKjo0kw9GAUzbBLG3quRtqAjR/NhZrZaMc7qpvc3kIaxJEmGj1Pc/fTSoPcQd78HaS+sjCaNp7fw/U9KrwcgD+nlSNz0SZQe921zBq2SseO9+P61LgH0xYhoIHcfhIxzm5rZ2CP8SzsQl3D83sCFZraSu3+MhHDfgyYT8JlQZNSgzjKw7wjC8PUyihScL4xWvyDDRsEuqGpUc5FFfyKj2EWmFN6lgBfdfV9gHao8v4V3rtSGk939iDgHA5DOXVHVp2/8/3mBd0rRTslw4pXUxkuR5uFyXqlg16qh293/cFUMncolwLwyilq5z93fR33QLLH5WURp9Rr7Gd57cr2R+NlJMkoRE6wi6nYrd9/C3ZdC1X23QkaYM9z9DTNbFlXC7DCjS2dzmhUOHjM7BEUiHQQ87R0oeO/uDwLvm9nVZjaVu5+MoksGIMmBdYCbrYsI8bcnriqOmyB9sb8DP7uKP92IDBe/A7uY2QJmNgc6jlN21nFaRIDNQKXgwoKo8M3jqHrrPmY2N/qtExJRSdXjoqS+uPsRwBdmdjUMLS4xGFgCSYB8ZWYzxj3ZnBC/uftNSLf1BVPVbdCYemrgGaSZO1Y4dacA/hIO6W5HaowlSTLcWEV/rCfSWFodeBsZHn6Jbaq1Rgz4KxICfReF8J6FvFUXlbYb7hLuJm2OF+IhgpkNRoO4gV4pRd1p8+dL3vLFkAfnuZJ3qEdMFmZGaaz3oqiarxrY5IZgqlZ5BIoW6wXc6u4XmtlKKFprLa9UXDIkOPtz1T7GQdpR13tJl6F8fcSAcE+k6/B11WfrIjHpmeL9MsjDX4iafuvu23fm660rEAP1Z6OfGZE+oSe6l04E3nL3k0xlzP+B0hN2i0n5LF5DP2U478mH3P3w+J8TxSQqSbo9VX3nVMBuQG9UKa83ivB9H0WQLY2e2d2y3zSzCYDTUATH4x15HKrO01kote7IeL8cKqKwBNKANHffOT6b0pUN0C2IZ8gP8Ww4CRWFGRifzYMilVcCDgnDYqckjM+zo+JX46ECM7e5+9Fm9n/IUX0Ciig7yFWZfl10365T65mZtC/WVAN1TjRv2gnohyLf70bnx9H9um8b9tkTOBrpNN9pZiegcfR9cW1fgzIvLnD3c+rxuzo7NcPukiRJWqJkFFsGhWO/5gopHxgepQ/d/bHyYCsGErcChyCv8Srufn+xz+IhMAIT4LFQRMgD8X5JYHrg4mjnYOBVd3+ihd00lNIxerS8vuRBLybgDwDnuPvXpc87pOR9o4lr6RYz+xCl171NJTpsFuDOklFsLFTtq5eZHenun5Z2NSWagB0V264PPOLSuOmFIsvmRuXrxwa+Lk9Q3P1qM1vOzAYCXyIP8jPAHa40y8liu2KS0e286+2Buz8FI36NlwzitwCHmNlHyGP6JvDXMIpdA/Qzs+Wq/8dw3JOPhlFsdCRkO6WZ/cWl5ZEk3ZqqvvN9M3sR6YrOjyqsPYrSmTdBFYQXRJNyzGxyd/+wo9vcKFxarJsDv3S0cTDGZ8UYbFsYKp/xprvfE86nadx9FzOb1lTYZlJgOjM7tBiTjOp4JfUVlKa/S+n9/Ch6bBsPyZDOSjzDXo7r7CuTtEnhbP0UPcsOA36NbddDY/cHgAvMbIs0jtUXV0R6cU++aGarobHvzSjab0+XTmEx5m3LPn83s7uB48zsa6Qh+JmZPYju59GQbMjbsd9u5+DNiLEkSUaIGCgNQulop5jZFSgE92EUQbanu99V43vt3tHGA+M49DDvj7THrkRpVKcA1wEnVhvrOjNWSdUqJuAPotSTr0ufFVEtPb1OOiSdHTMr0hqXR+kMiyD9hRdRquPDMDQVovjOisDMEUW0E9KeWsfd/2fSb7gMGSCvjO2LypRlD954aBA8HhJHLbRrTgf+A/wfsGV5cFPnQ5E0g6mS7n4oWnX7MIpdiIS/13H370v3UrP9gzVNnzwFGcUODYPqPWhC/zoywA32GlVOk6Q7Un1fmSoLv+gqlLEbKoTyEopIMaRH9jPwgNe5WmVSoSpy7G/A3O6+jpldhBye+8ZnNyAJjGXc/cnu9owLQ0QRvX+eqfLfDcAe7n5+I9s2spjZXkicfRFXeu+qyIi9azgnd0Jjn1U8ZSM6lBjv/gcZKNcBvnD3n83MkE2nGJ/OD8zqKjJVaz+roOjd55CczejIyPtq7HttVBjk8/r+os5HRowlSTKivBvLaaaS7JO4+xIAZvYSsKWZ3YeckUMjZupgFDN3v9nMPkCRYq9Eu9ZE6RlrosnqrWa2mdex7Hl7EhPwaVBBgcuQntrXpc+mBwabWR9gNDM7w6WL1G2IqMUNkfFrJpRmeRVK6b3QpDF3KrCKmc1QOn53mtkj8fpUM/sZVe25CbgJeNjdrwzj1yHAIhEFNDStMl5PBgxy92OjPTcDA1GKAsAdZrZid5owdEbCIL4z6qPeMLMzkHd0kLv/YNLcWMnM/ufuT7ewnz/NbBZUOfP2UqTY9ej6WzqMa32AA8xsS3dvrWJUkozy1Hju9wI2MlU+fAw9p59x93fM7Nx4/9c0inUsVRF+h5vZBWZ2G/AxmpBjqshcRK5sbmbPdDfHXDw3tkbX8FXISPjXUcAoNiaKqj85jGKDkBzBbWEU6wm8h8bUPzWupd2L0rjz1TgH35T7xrhvC4P2Qqji75pmtoS7b1W9P3e/zcyedEWpToVSpf/nFTmaN7xKrsW6SfZDiu8nSTJCRDTEYTHp74cEIQt+Bz6PwVK/OrejSHl61t2vdpVkH4y8IYPd/SZXWtNbSPC3KzEBSi/5MB5gvWCoN+ix2OZlVDXoxpi0dyfGRCk4cyEP57bufmAYxfqgVICB6Bg1cQTFwLYoInGuu1+L9BvuB04ws21in0ug9LvesW154nAoEiPub2bHx+rBSJvjfBQBMbRAgIUYddLxuPtbYSDrh8SEN41rYAwUbbgvMF2LOxHjAPeUjGL3oInCpcANEb15OjAEpXgnSVKFu+8B/BNFolyEnBEnmdk6wNcoPX0DkFB4RK0kHYRVCidsgSLuL0D922DkcNzC3dcH7kBGzm6Hu7+MnvWLogyJsxvcpJHGpaN5GXCPma2JDCYHA/OY2aHAZihT5LtSdNK4jWpvd6Fq3Pk78GI4Zptg0iLbAAUHDAT6RhZDLQrD1+bAOKVo0B6FUawYI4eT/saIWBulyVTKJElGmBg8jYs8hwe4+wMRyfQIsCPqnK9D2gvfuPtvHdCm/qjM+DYxER4fhRzPAxzr7m/Fdl0lpXIt5LHb1N0fDOPXA8DR7n5CabtdUIn3jbpThJJJB+UDZIj9LtaNjaqjTo+0Es5294eb+X6RQjcVMmZ8jLy/F6M0yQ2QCP8zMQAc3d0/q04dibSTZ939LjP7KzLYOap+OS6qVundxevWWYnz/CBKn30TGT53Ac5096vMbF50jp9oqY8Io9hjqOjH1rHuLOBjdz+wA35KknRJrGlK+p5Af3ff2aRjtCTwhLtfYips8qW7vxDPwVc8tfs6jBrPuKWQgWzVMAqVt+22zzUzG9fdv2l0O9qTGDe/BOzu7tea2dJoTHQMMopdENutiYoPDSKKqzaqzd2B6jFJVV+6EEqBXA0Vl9on1h8DXN1cNLyZ/QU4EhWveq/G51OhYmlToCjBLX0Uzk5Jw1iSJCONSYj8SGSwWRrpM51kZmciHZ5Lzay3V1UIrGN7xnVV0RkfPbBnR6XPL4vPyzoa8wIfuPvHHdG24aFktFkLicWvhQyOH7v7QbHNGBHyvhCqpLhedx2gwtCqk2cCvyFv5yooKmEMFL21JXC+VwnHmqrz7IrSMs8ijL3Afe5+d3jMrgFuAU4ojHCl7x8EzOXua8T7LZGA//1oQHmfu5/W/r84GV4i+mR1pDE2Pop8vSMM/QOAK4C93f325oxjcb9t5+6bd2DTk2SUoMYEb2LU3z6J0pS/NLOpUZ/8kpcqVyeNIaKoN3D3pVvYZmagL9IeO6bDGpe0O2Y2sUfhIlNRq+0jSrD4vBewFUrBu6VBzUwYWsV7LeAzpK97GHCJu5/SyveKOcZ8wJ/u/lzV51Mh6Yg33X2nMITuAqwxqhmDCzKtI0mSkSI61luR1+h8FKl1kqnE99vAByZx/A9M5aw7gu9MefjroWi1slGsR8kotgwaeN/fGdMQ44Fl7n49+i1vAT2AZ030CqNYL2QA/LAwiplZV0sbHWnCKHY+gLtvBmwHTIYMIBcgvbA9gR3i+ihzL3AoMMTdr0Di+e9ot7YRikK8C7gQWLf6+Iah8hszu87MBri0Rl5GRrlJgXnN7HIz621m44QHNulg4n66BTgWmBg4EHjdzI6lcq4GAUeY2TSlvqJHeT/u/mTZKGalNNki/SBJktoUz7bSqnmAvyAtzS/NbDqUtuVUqg8Dw96LSccQqYIvmdnGtT43s22Bo4HlgBRl7+J402rem6CiGIVBDKRFthIyxiQNIvrKLdE9d5e734Mix36O7InyttXjmEKK5plmjGKXoTTLnWL16MCPjML3d4rvJ0kyUpQ61ueqPloHlf2dABnh3yRKAHdAm/4E/jSzh4GfqoxiheFoYZSDf3+07SIz27SzpWqUj28MSMamUs79t0jpGoi03M6FoSLwc5sE4zvV76kz0wK93H1tU+XAjVH1wD2A79D19zmq7jlULDiMJbeZ2d1eSfddG5gD6Uf9hCKIbjFpVD2AxNaLSpRFSe3NzGwr4AszmxHYG11bZ0Qq5o5I32oiYFsze6Vq8JnUmdL99LqZre7uH5vZqei6eAYVcLgUVTQtdDamBNY2s6vd/cNm9vtn6XWG4idJK5TvE1dBlH8CK5rZC6ioyrjoXnwnjDG9UAXgP6wbp+41gtIzbleTLmN15P3eyBG1Norw+82k87mVu5/cuJYnI0uMefoioydxbidBz8hL3P3JRravu+Pub5vZHcB/3f1f0Te+Z2YvA5uZNIm/B3aOvrNVGRmTJM3pyDk8vZntGvuYF3gothklK9FmKmWSJO1OTDR3QOWrt0faTZ+4+wlmdgrSfPpXB7epZ2EMiUix9YFFgDXd/c1IfVsMPTw6rTckou+OA3YG3kcD0amA59z9dDO7BHn2HkOGoa1dhRK6FWY2Nwr5Pg1pI0yGUmrPr2HErf7useiafQWl9zyODGt7oeizIV6VFmxNtR4mQNVEn0EREP8ubTc2cDIScL+8HX5qMoJExMroqJ+60N2vNrMpgKeRDtkmKKpsUyTQv62HRmGSJO1DVd85K4p+6IGic79DlYKfiXUTII2rnLx0MFWGsPI52wP1leu4+5uxblx07jZ090sb1eakfTCz2YDP3P3TiCS6F7jBQ8cqaQzNGbkiO2d74H/Aq8ACwAB3X6UN++wN3IZ0cQeHkex+ZAi9C3jR3d9taR9dmYwYS5KkHlyC0h/ORManpYG/m9lFQB/gy2LDtngv2oOSUWwppDv1KuoDTzazge5+vpnd18mNYubuN5uZowqK4yDjzRXufp+ZnQSsDEzj7t/FxP9YM1vPqzSxRlWK68ndnw9j5yTIqDEFcEFrRrHgZqRHtre7/zfSJndBYvxXAL+b2WyuCqjAMBFDX5jZhcB/qoxio6N74VNkeEkaSPQ7v5jZucA2psIhi6Mqr5sjo9lglOa1vbu/1VH9VZJ0F9z9z1K//ZqZPY0iOP+LnBKXuvtRAGZ2E7AucFW8z/uxg6iK8CuMYoOQE3SVklFsYmTMvMzdTzVpxa3g7uc0oNlJO+Dur5jZ4ma2IjJcX+Ghc5s0juq+r2SwnhfNsy529/8C15jZjWY2nrt/HdvW7Dvd/Wcz27nkUJ8DZUcMcUm3LBjG0d/c/alRrQ/OiLEkSeqCSXNnHDSAdeA+1JGeaGb7ItHrB70Fges6tGlZpB/0HgoB/8DMjgSed/erStt12jSN4liFV6eXVyoxXoJST95GRp31IuR9WuCj6ginUZnSMRqAtNn60UajWOm7o7v7rzEA2BCYHBnFnkHRYJMCu7r7v6s86c158Pqgwcpg4J/u3lwJ7aQDKZ3vGZGexo/AisCfwD7AXMC+7v7GqDYATJLORPX9FZPw9dx9C5MmZE+U3nOWuz9V2m6UTOnp7ITjbV/gGXe/O9ZNjJ6Pn7j7NqY09N2QY+kCd9+2Ue1NRpwYSw1BDr1Pi/OddE7M7HTgSXe/ON4fBCzs7iuY2ZTu/r9Y32rfGQ7mhZE0yDZImmRblE2xr7vfUb9f0vGk+H6SJHUhOtuJUATG3Sjs+kQzWxyF9T4AHGZmq3XgZPNDFFp8rbt/EO3cB/jIzNYys/3NbFGPPPwOatNwURwrd/+5ZBS7AxnF1nD33VBE0vGx3X+7k1EMmnjRfkEGrIvaGClW/u7vJi2VzZB22WVUjGKTxOszTTpVXuP7Qwnj5OYo3eTNNIp1Hkr305soCnPF+GgvmhrFRgsDWgp/J0kdqNF39kb9N8CYqP+cAXjFzAabZBmKiLO8LzuecWmqtTkxcDYynGwTkdYHoLlmP6C3mWXUWNfkHWB3d7+0ZATtlGPkBIBPgIFm1tPMjkD36uFmNilwnJkdG46IP5s7j2Y2mpmNiYzaN6PiKKui+cVXSKplXzObcFS6FjJiLEmSumJmk7n7R4VnwlTO+xyUDjE7EunfvqM8vmY2prv/VHq/IrAGGoT/Bxkwtu8qHjGTltbhwCB3/7G0vk/5fXfFzMZx929H4vsD0KDiGeAM4FfgSJe46TrAFkin7heTftgv7v5rfHcSVN3yIOBGZBS7aWR+T1JfYoB3ILAQsEtEBDbxqsY2G6K08Nu8GUH+JElGjkhzng5JM0yNns9zoD717Vj+7qV0zAY1tdthEmWfx93vNbO+SH/oVXffOibg+6Hz9Q93PzK+cxZwhLu/06BmJ8koS1X2wnnAWLEcDvyMxqsDgeuAOYGVWsuOiTHwV6gQyl5IauIBFPDQ00cxnbnUGEuSpC6UOuhPYyK5m5m96arsdxewgEsv6+GOTIMojGKR6tkbWAqJbd/r7u+a2atIF61LGMZcWlqrlDw/5qoe9WNOFGBkjGLx/VcBzOxilF53MIo6BFWY/CCMYgsiofYfzewhl6bdT+gaO9Dd7xuZdiQdQ0SF3YI0VJoYxcxsHFRp90g0OHwU+L35vSVJMiJYpQriVuG8egdpjq2AUvOOBR4B9geuR9HS3fpZ19G4+5dIhB13/97Mznf388Iotg+KFFsYOMvMvnH3M4pUys4sV5EkXZUYvxR95+DCQR6SIDsgJ+/8Lh3c3cr3YHP3pLu/GhG5nwCTuvtzZrYuMpQVFSonQ0ay97v6vCMjxpIk6RBM+l4nAWuhXPU+qGJRwzohU/XAK4E93P2lMCzdALzm7vuaWd8Y8HXpjj4ZecxsduBbd38v3m+C0kQ2QqkkzwPPocqFb5a+l9dOF6XK+zou8rQui4oqHBbrZ0Kl7D/10O1IkmTkqe47zWwXYHXgKKRP+quZLYz64F3T0NIYavSTJyMn0p4xAZ8CVQE/ARjTo8BRGseSpD5U3ZMToT6zB0qH/TLGLashTdXZgZ3Cud6s5piZrQIcgfraB2K+NB7S3v0p9rOLd3HNsdQYS5KkQ4iImb+hEuw9kRBrow0GY6HKc7/E+32RV/oMkxj3EWY2T3hhRpkc+mT4cfeXS0axLVGayGbIGLYkih6aAQ0Uyt9r9DWejCBVmmJzIKPY/SWj2E5Iy29H4Hozm6UxLU2SUY8afeeYwIlUjGITognfF2lgaRzl8+Tu36Bn4ZAwivVw6bneSYz/zOyimID/EZH7SZK0I1X35GfAE8BuYRSbB/WjW6Hqv/8G7i00x2rtLz67DY17NzSzGYAxkETIu+6+JtKAHBipl12WjBhLkqRDKDwYptLdH6P+55fWvtcB7VoLaQp9A3wAnA98gTRM5gIeBq7x1IZKADObHHgSGcUeRsKksyH9sW9RivBljWth0t6EUfx2lG59XKzrhVIqn3X3y81sfZQ+tLa7v9W41ibJqEVV9EOPMKhMhHRyXnb3Haq3i/fLexfRCh0VqHH8e7r772Y2PbAlMCXqM7cApnD3jRrU1CTpFtS4Jw3YDqVU/gYs76pUeTZwgru/1tq+zKxfGNgOA2Zw9/Xi88lRkarJgIXc/ev6/bL6kZb6JEk6hKJzdvf3XOLkvza4SUVHfz2qtLKlu2+AjGObofS4mZFnZcGGNTLpVLiE1udx9wdQAYnZgOvd/dlIofzCzBYws/khKzeNIvwVeK8wigG4+2/ABGiSh7tfiSbqczWigUkyqlIV/fCHqcjJIzQ1ivWomgAOAe40CVAnHUB1hJ9LZxMUSTITsFdMvM8C+oVxE9D567CGJkk3ocY96cBUSBbkOOAeMyvmOu+Vt62+J0tzuC9j1e/IYVhsuxSqPrst8J2ZTdnev6cjSMNYkiQNoTOkmFUZ696KEOB9UC7+di6h/o+Bx8xsKzNbHNLYkfCVmY2OylW/7+7F4OAUVLVnAHCima3eGa7zZKT5AqWAY2YLmtnEsf4Y4BszWz3en+Du15rZOKGrkyRJO+Pu36HKwGWjWFlEegiqND0A+DONY40jImunQOfr09B1XQF4y90/M7O+4aD8I8dVSdIhfA0sDeDuxwLboLHMEma2l5ldXEp1HsZgbWbzmNkiyBE4xMw2RPqB+yINs4eAIcBRkbbZpUjDWJIkSYXRgB9QLv5vZrYycB7SEPoGOMXMVk1jR/fG3f+IqMezgAXMbGIzOwSYGuku/IaETXcys/Ea19JkZDDRExnGXjGzPiiadC4Ad38F9QuLxfvvzWx8VMDjFTObuSENT5JRlMJ44u4XxvueVUaxPVC63pPu/rq7bw1MbGYLNaK93Z2IrP0a2MfM5kAplXMB10Tk33vAabFtarkmSZ1x96OBb83sXjOb0t1vAO5GEV+9gI+AB6v71hKfA2cD46BCaksBywDbu/v9ZrYjsAGKKDvKVFW4y5CGsSRJkiAE1rcPXYzxgZWB24AJgU+BNYFVzGzMRrYzaTzh5b4ZGU2/R4UcjkcDgp2AXYE/gF/Ca550MVz8Dtzo7t+6+4+oPzjezOaNzYoCHkWV27OB/wDbA1eEvk6SJO1AC+l6RaTYOsAqyGGxa2yzqrs/mUaXjqVkxNwLeB84GJgYuAB4BngBeB1418yuLtJh8zwlSX2wKHbh7oOBa4AfzWySkrzNOO6+N/AAMF98ZwEzW6LYh6sI1YZovLsWkpTYEnjSzLZDhu/D3H0zlIHzQ3FPd4V7u2ejG5AkSdJJ+QXoDxwN3ARchSbA90WKJTCsuGXSPSil4b4TaZWTARO7+0NmthFwLXCFu/8UabiPuPsbjWxzMmKUhL/N3W+L9IJzzew7pEl4XkQGngt85e7bxPbLI0PpPxrT8iTpHpjZgShKd3N3/5eZvQnsG1GeP7v7n2F0aZJ2mdSPON6jxbEfYmbjuvs3ZjYu8DRwh7vvBGBmq6CUy/fyPCVJfXD3P0v35FmmYmhbAQeg8cvBZtYXOAyY3cyuRanPc1Tt5yUzWxUZ00Z3909MVboXBq4KpzFIn9mArc3sUne/u7PPmTJiLEmSpDZjo7S4id39XSS0/iJwtZktZWbbmtnS6eFMwtt2MXCgma3i7m8jb9vVZjYY+DtwtJktVXwnBh9JF6K412PQty6wGxLf/xA4E0WYPgkQHtYJgecb09ok6Vb0QAV0/mVmo7v7O0jnZjngJDM7tqSbk3OfDiIm4sX46Nt47n0I3F8yis0BTAdsY2an53lKkvrh7n+WXr8HTG1mFwHjA7MAs8WYdmoUffuau/+3xn4+cfevUDYNqEr7lYVRzMzORKmWRRGzE4s5U91+XDtgnbx9SZIkDcPMVkPRHke5+2URGTQx8q78D6VWHuTuNzWwmUknwcwGAnsgUf6f0PWxKPAGCk0/CRgELBt/13f3nxvT2qQ9iJTrS5AuxwHAU8DjQF/gWTRQfM3MZgU+d/fPGtbYJBnFqI4+CE3AP1CUwi7A+ijlZxE00RvU2SdmozpmtpG7Xxav50fOhbXRuRoA/AVYM89TktSPct9pZoVm2DdIBmIdJAuxHLA88Ie7HzEc+14QVfM+DDgV2BMYD5jM3Tt1MZRMpUySJKlBERliZn8CC4ZQ7C8odHgRYCOkN7SPmT3q7l+Y2dyo2tJ3jWt50gjiernVzB4DvgO2RuHnt7v7tbHNGsgzvhVwcBrFRgmmBb4PkW/M7GbgZzQY/Nrdv47tDkQCt+c3opFJMirSnOaYma2LCmVsGlFk1wJ3IcPLK7HNaOXoiaS+lFK4LiutXhwVqzkVReDugqKtZwFei+916tSrJOmKVKU6b1NKdV4RjVMGufs/gX9GSvpQ2tB3/g787u7PmdkmwNXA3Cjdfej3O+O9nWGqSZIkNSilTd2KJrUDgMXc/VPgdGA+d38WRQitaGYnAWcAvRvW6KRhlDTHvgImBRYAHikZxUZz90+AL4G93f3+Yn2Dmpy0A+7+nLuvD2AqsjAR8Li7v1MYxUI/5zN3T6NYknQMMwPHhVGsD4rc/QKJvRfGlnKaX1JnmplIT42cR4cAj6DI6p+Ad0vf89B1TJKkHSn3gWEUGx8ZqFd39zvNbIz47MfyPRjfa6mo1C/A9GY2h7v/B1gVRaI9b2b7AxeY2UqdUYomB+RJkiTNUDJ2FAO6081sdtTpr2tmvSNHfzZUifBSd/8sjR3dG3f/H7Cvu18OTSdhLgH+b81sMTNbKCdnoxSTI6/o+8WK0NSZF3gjz3OSdBi/AGuF0PtiKML7udCuuhs4DSoOsAa2s7vzFbANgLsfgybl9wEzmdkJZnZOSXMsjWNJ0s6UI7bCsfsGirbF3X+BoWPYP+L1Qma2HnC7ma3ezD5fBg4BLjezrd39Q3c/C5gdmBEVNDvCzFbNiLEkSZIuiLs/BewFXAn8H/Cqu/9sZruj1LitkLFszkzPSNz9Y2iaBhKTsHGQEXVp4PwQ60+P+KjBpKhq7dMAZjYmOteLAjd0tgFgkoyquPtRwMeoOvDGwOvufrSZXYqqH35tZuebqh/mfdkg3P1g4Cszu83MJnH3myLK+kCUYvk+cK+Z9fKsUpkkdaMUObYr8IiZjVWsL2mRbQccjfTCTmxOX7mQokGp0a+Voss+AKZBae1DgDnr9oNGkBTfT5IkaQPFw8HM5gR+BN4BNgeOBJZ19xfMbBbgbVdFlyRpQkQS9gZuR0L87wCXAau6+39Cx27ciDhLuhA1RMCXBJYAlgG2dve3Gta4JOlGlPVvzGw6V5VgzOwKVGl6e3f/xMw2BP7t7s/E5z3S+NJxVJ2ndVCxkq/d/UszOwQVKznZzPYGXnL3O8xsY+CFiEhJkqQdqdYOK/eJZrYbqrC+IvB86d59BPjY3ddpZp+GggpecPe7zewo4GF3v83M+gHfAlO4+7udQXMsI8aSJEnaQCnq50V3fxOJw/4NmCOMYr3c/XV3/9VUGWsomVqZmNmMwDLu/iNwDtDf3Z9HlSvfjcHDUcC6DWxmMoKE0Xx0M+tnKn2+OuBI/DuNYknSQVTp5hRGsSeAXsA6YRTrA3wG/MXM9s90vY4nztNo8foaJNi9T3x8IzCvmY0d0X+fmNk9qF/9n6lCeJIk7Uh1tkvJKLYBivBa2t2fLRnF7kaRnb+Y2ZnN7NOBF4CTTNUqpwKWio/nRFWDbzCzlRttFIM0jCVJkowovyBPR18Ad/8NhnpcispYs5vZssAVZjZeoxqadAp6A4cXUYXAFmY2Iapi+jswPvApcE0D25iMPDOgyId9gGPc/f1Wtk+SpJ2pMcG6DNggjF/9CE1QJMj/LXBTWUcn6RjKE/HQa50sHAvvI+fj9PHxOvF6IuCHcED26+j2Jkl3w8wmAZYFBpcjNc3sLtTVLuPuGwNzRkTnMLj73cCewMno3n40PtoA6ANsQsVw1lDSMJYkSTICuPu/gf2A9QrvdFVqwB4o7HhB4FaPCnVJ98Td/wUcDlwBbAg8HeuLidgQYCY0UUu6IJFC/bS7n+zuvxbCtUmSNIZS5NiphfMKVQxeGhnLVgMuBD4C/tKINiZNztMmKNL2GOD9iMY/AugPLImEwTePyL5DzWyJRrU5SboJY6NAgNeKFWZ2C7AcUFTknhUZvN6ttQMAd78NVac8EHgzVl8JfOvurwBbolT3htKz9U2SJEmSWrj7LWZ2a6RRlY1ihwProbD/D1yVXjCz+4GN3P2jxrU6aQQRjXCjmX1ORIe5++fx2f5ogLGIqyx2w3UWkhEjz1uSdB6auR9nB+5x9+PNbC8UvfAc0CTlOTXHOo7yGMrdNzezvu7+vZntihxGh0b07bbFd8zsR2TQTJKkDoTBelmk8fe/WFekT24HPG5m5wKTAT8Tjl0zm8Ddh3HyuvunEel5gJmdDjwF7G1ms7v7I/HdiYDRIuW9w8fCaRhLkiQZCWJAZyWj2D6oNPxCZQOYmT0NfJVGse5JSaPuUah4yM3sMGREXSkGDU3ET5MkSZJ25QdgJeB4dz/GzD5GxpfPItK7LzLE/JHGsY6j0IZz8b2ZjQvMARwRkWM9SzIVpwFThd5rkiR1IOY3l7r7DwBmti8wqbvPEe/XAWYFrkNBAK+aWX/gTjN7wd03qLHPL83sSuBElEXxMzBTGLqPAfoB45rZge5+Swf8zCZkVcokSZJ2wswWBfYF9nD3N0rrH0IRQuuU1mVUUDfFzMZEabYOzA9s4e4fpVEsSZKk/pjZOcDkwObu/lmsOwxYCEWQLQEsVxhiksZgZucB07r7MqV1O6Lzs6O7f55jqSTpeMxsDOB64Gh3fyjW9QfOBV4BxgN+c/etq75nYXAbiET4v0EFqfZHdqkdzGxO4ExgY3f/Twf9JCA1xpIkSdqTCYE7KOXZm9mzyBvtpXUTAmuGqGXS/VgaCe1fB6ydRrEkSZL6U6qCuDVwDzCDmfWNj78EnnT3A4EnkOYNZraomc3WiPZ2V0qaY4ORYHexfh+kEXch8FVsk0axJKkzxT1Zoj+KFvsuPu8PXAA85+67ufvmwBxm1kS7sZRlc6u77+nuh6Ism9ncfYfY7Avgc+DXFv5/XUjDWJIkSTtgZmMBawDvufvPse5J4A13nwx428yuj/z6hVH63KKNam/SUO4JgfYXImXE0iiWJElSXyJdrzCOnQi8AAwO49i1wKRmNqm7/w2428xOBLYHxjCz3o1pdfej0ByLtw+a2SwRPbYkcAbwcKa4JknHUcMAvSjwoLs/F0axi4H5gIMBIiLsB6q0G5vZl6MIMSKFem7ga3d/38wWMLPFO8oAnhpjSZIk7cPvwHEe5YzN7EVkJNsQwN33CbHJe5HQ7w3ufr2ZrQ/c7+6fNqrhScdSqo5WvE+Pd5IkSQdQdkK4+09mNg8wv7tvbGYzAIshI9kCqIraN6hC4s8NaXA3JYyYPVEV57GAj4Gt04mUJI3H3S8ws8fNbDqUCvkwMo69bmZXA5MADwI/ApjZjC1oAr4CnGJmkwITIW3BY8xsRuBG4H4z+9JVvbKupMZYkiRJHTCzud39+dL7MYAVgK2Bc9z9ZjPbBFgeuM7db2xMS5MkSZKke1HWpjKzy4FewILAXMAywMpoUrYm8F93P6QxLe3emFnvslEyNcWSpLFU9Z17AHO6+6bx/jHgJuBmVHDsk8iouQI4xN2faWafywCrAW+iAII/gdvj9RPAYGCHehvH0jCWJElSZ8xsPDTIXgqFHl9uZpujQfh97n5thB3/y93fbXZHSZIkSZK0C2VtRzObBmnbzA7sAtwGXO/uP5rZ+O7+VeNamiRJ0rmJ6LErgfXd/e1Y1wPYEZjY3fdr434WAC4CTnD3s2Pd1MBo7v5OvK+LgTw1xpIkSepIPBRWQYax+8Iothkyij0YRrEtgD2B28xs5gY2N0mSJEm6BZGuVwi9v+Pu3wHLImH+K939x/hsGKNYaOEkSZJ0S2oI4s8A/AZ8X1o3GaoA/Egb9tfTzMYHrgFOc/ezLXD392oZxdpb+zENY0mSJHUkBGJfAm5396sifXIh4IF4vzaqUrg7Ep+8wsxmalyLkyRJkqR7UCPqYAJgK3f/vVhRngCa2VhmtirSwBnYQc1MkiTpVNToOxdBAQCfwtCiZLsDU9IGw5i7/x5OiMXc/dTif9T4P33MbGIzOwvY0czGGdnfUpCplEmSJB1ERIYtAdxWihRbH+gHLOfuX0eKZX/g0BSZTZIkSZKOxcx2QhFjn1dFJ6wNzAbMC5yAqlp+mxUSkyRJRBTNmB8YhPrL9d39m3ba9wCUhTMz8BlKt/zUzPoUEb4jtf80jCVJknQMZjY7MFtEim2KBH4vRYKTA4AVigG2mc0C/JSaY0mSJElSf8qaYzU+G4yivZ9CEd//MbOJgIOBH919zw5sapIkSacipGP6AUcBnwK/A0e7+7fttP/ZgV2Bd4Cn3P3eWL85MsLt6e5vjNT/SMNYkiRJx2JmWwIroiott7r7b2Z2MHAXqr7SCzgRuMXd72hYQ5MkSZKkm2NmiwHrAQ8D97r7V2bWDzgQ+BlYHHjB3XdoYDOTJEkahpn1AdZCumIXAl+7+2/ttO/xkVHsV+AGd38t1hcOCwPmBjYuPhsRUmMsSZKk43kWGcXuLR4a7n4g8EykbPQCHBgpz0eSJEmSJCPNHCht56Ewik0I7A1MAxzj7osA45nZhY1rYpIkSeOIVMYr3f0Yd/+sPYxiJX3HCYBJgYdLRrGNgcWAu919K+As4FQzG3NE/18axpIkSToYd38RuCkqYAFDUzh+jbdXAT2LcsdJkiRJknQ8ZrYAsBl6Zn8aRrEDgL6oCtuGAO6+IfBDRDYkSZJ0O8pFS9ppf0Vq4z+Aj9z9URiaPrk58Lu7XxPbPA18g4ILRog0jCVJkjSAso5JWdfEzM5EYr7bxvvqcshJkiRJknQMfYCj3P3F0BTbH/gOyR2sDKxnZrsCuPuOQFG1MkmSJBlJIm39SeDQeL89sChwPTCfmf0j0t1vBv5baJqNiJOiZ7u1OkmSJBlR3MxGA24EvgX+NvSDFIJMkiRJkkbxHDBRvF4aFc3ZxN3fhKHpPDubWS9gRlRMZy4ze8bdP2pEg5MkSUYhvgMeRbECUyGj2F3ufomZvQRcDXyOKlQejzY8CVjMzDYcHkH+FN9PkiRpIGY2M7AmMAuK4t28uapYSZIkSZI0DjM7HxjT3TeoWj8ZsAUwJYom+xxY2t2v6/BGJkmSjKKY2aTAr+7+pZktCBwNrOjuP8XnxyNnxpvAQGBTd3+9TftOw1iSJEljiNLG6yFvyOfu/kSDm5QkSZIkSRVVkgenAdO6+8rxflZgDSTGfwLwJbAzsC7wqLsPbkSbkyRJRlViDnUa8EeksWNmhyLtx8Pc/WUz+xuqVrlBW/TPUmMsSZKkQbj7H6iCyy2FUSw1xZIkSZKkc+Huf4bkQaEl9pqZTWxms6D0yf7A8cBHwK7AD8CmSHPszAY1O0mSZFSlJ9APuBXAzI4B1gHecveXY5sPY2kTaRhLkiRpLE3CdlNTLEmSJEk6H1XGsT2AH1FltGmRUewrYC8UPTa5uz/l7usDmNmijWhzkiTJqIi7/wIcBdxpZkcBk6JI3dXN7HQz2w4YArxQRIuZ2eQt7TMNY0mSJA0kDWFJkiRJ0jUoa4C6+/fAG8DJyCi2E/AbcACqllZUq9zO3R8zs/Ua0OQkSZJREnd/FjkmZkXi+/cgzbGFgE+Av7r7BQBmtjewXUv7S8NYkiRJkiRJkiRJGylkD2LS9T6wHzA6cH8I7q8LjGdmk8T2UwOLFxFnSZIkycjj7m8DGwIvxioDXnL3G9z9zvKmwD0t7Ss75yRJkiRJkiRJkjZSjvZ29++Ax4A73f2RWPc/4Ah3/yQ2Ww1NzHLulSRJ0o64+/fu/oeZjQ1sDzxf/tzMtkfOin+3tJ+e9WtikiRJkiRJkiTJqImZmYury+sAc/df4/1awNbAWm2pjJYkSZKMEKOjCN57ixVmtj4wGNja3T8pVxiuxlLeJkmSJEmSJEmSZOQopVh6vF8OTcqudfdrW5qUJUmSJCOHmU3r7v+N19ugAilHuPsthSOj2e+mYSxJkiRJkiRJkmTEqZ50mdkWwDrA9cDN7v5pwxqXJEnSTTCzdYHpgU2Bzdz9n236XhrGkiRJkiRJkiRJRp6YlM2ANG12Ap5z9x8b26okSZJRHzNbBZgNeA941N3/11qk2NDvpmEsSZIkSZIkSZJk5DCzCYBlgR+BZ939owY3KUmSpNtgZr3d/efS+zanr6dhLEmSJEmSJEmSpJ1JTbEkSZKuQRrGkiRJkiRJkiRJkiRJkm7JaI1uQJIkSZIkSZIkSZIkSZI0gjSMJUmSJEmSJEmSJEmSJN2SNIwlSZIkSZIkSZIkSZIk3ZI0jCVJkiRJkiRJkiRJkiTdkjSMJUmSJEmSJEmSJEmSJN2S/wfmZboYQ0NskwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bars(df, columns=[\"train_main_time_diff\", \"execute_main_time_diff\"], title=\"Runtime change\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e02a1f75",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
